PERSPECTIVES ON THE USE OF ARTIFICIAL

INTELLIGENCE IN THE GEOSPATIAL SECTOR

GEOSPATIAL MEDIA AND COMMUNICATIONS

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EXECUTIVE SUMMARY The report on Pe rspectives on the use of Artificial Intelligence (AI) in the geospatial sector’, is prepared for a study commissioned by the Joint Research Centre (JRC), European Commission (EC). The report aims to assist and support the JRC in its intent to compile perspectives on AI and AI-based technologies (machine learning/deep learning, robotics, natural language processing, etc.) via questionnaire-based surveys and also one-on-one interactions (audio-recorded and compiled written interviews) conducted at the Geospatial World Forum 2019 in Amsterdam, The Netherlands and through follow up calls and interviews. The assessment report aims to cover the varied aspects of the utilization and maturity of AI, i.e. the current use, future plans, benefits and challenges, technology and human resource investment and the future of AI in four major industry segments namely,

• Construction and Engineering, • Earth Observation, • Location Analytics and Business Intelligence, and • Smart Cities

which have been taken into consideration after due deliberation with the JRC, EC.

The report is a compilation of views and feedbacks of the global perspective of geospatial technology companies, and users in the four sectors identified for the study. The study aims to provide an understanding of the pursuance of AI and AI-based technologies in different sectors to increase efficiency and flexibility, ensure intuitive interaction and cooperation, and achieve productivity and competitiveness. The study emphasizes AI to be essentially an automation of the maximum sequence of decisions originating from prescriptive analytics and ability to give real-time feedback data to enhance prescriptive models. This exceptional ability of AI to adapt and learn, provides prescribed decisions (better than the previous decisions) enables it to execute actions following automated decisions. As organizations generate more and more data the analytical might of AI is expected to provide greater heights in terms of decision making as well as profitability. The study aims to analyze if this analytical might of AI is in reality being used in the four sectors identified or the use of AI is still merely assumption based. In summary, from the online-survey responses and also the one-on-one interactions, the study finds the current adoption of AI and AI based technologies to be the highest in the earth observation sector whereas it is found to be the lowest in the construction and engineering and the smart cities sector. In earth observation, AI and AI-based technologies such as image recognition/computer vision, machine/deep learning and NLP are being implemented in downstream imagery analytics and development of value-added services (VAS). The technology is, thus, foreseen to be important for a complete software solution to bring in new insights and improve the predictive capabilities of the earth observation ecosystem. The second sector which is found to adopt AI and AI-based technologies as part of their solutions ecosystem is the location analytics and business intelligence sector. AI-based technologies such as machine learning/deep learning, etc., simplifies the manual process of transforming the vast amount of real-time and consistently changing location data gathered from the multitude of sensors. AI-based algorithms parses data to identify patterns by providing labels and quantifiable attributes to the location data

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to provide actionable intelligence. At present, in the location analytics and business intelligence sector, the logistics and supply chain, the retail segment and the insurance companies are found to be encouraging the use of AI and AI-based technologies. Concurrently, the low adoption of AI in construction and engineering is not a surprise given the sector has the lowest pace in digitalization and the significant adoption of new technologies such as AI, augmented reality/virtual reality (AR/VR), construction robotics, is expected to take more adoption time as compared to other sectors. The study finds the construction and engineering firms to be optimistic about the possibilities that implementation of AI and AI-based technologies can have on the efficiency and productivity of the sector; however, the current adoption of AI and AI-based technologies across the construction lifecycle is relatively low. Today, more start-ups are emerging in the field of AI-based solutions to enable the digital transformation of the construction industry. Similarly, in the smart city sector, the adoption of AI is found to be at a nascent stage. While the industry recognizes the benefits AI can bring to smart city projects – the adoption of the same is found to be low in the entire paradigm of a smart city project. Interestingly, the only aspect in which AI and AI-based technology (along with IoT sensors, cameras, etc.) is currently being used by smart city project owners and technology providers is for resolving traffic management issues. Further, at present, the use of AI in smart city projects is in pilot phases wherein the use of technology is not in full implementation in the entire expanse of the project. On a whole, while the geospatial industry and the users of geospatial technology are found to be enthusiastic about AI and AI-based technologies as a part of their solution packages and are finding means in which AI technology solutions can assist them; the adoption of technology across the various industry sectors is found to be relatively low. It is found that the geospatial industry finds it difficult to adopt AI based technology owing to four-critical factors: human capital, knowledge gap, lack of policy/mandate and the irregular flow and availability of data. The industry together and along with various industry associations is looking to address these challenges.

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METHODOLOGY For the report, ‘Perspectives on the use of Artificial Intelligence in the geospatial sector’, a two-fold research strategy has been adopted. For the purpose of this study – both primary and secondary research have been conducted in confluence and after discussions with the JRC, EC. The two-fold research methodology is defined as follows: a. PRIMARY RESEARCH

• The team of Geospatial Media and Communications, along with JRC, identified the

relevant stakeholders for survey interviews from the participating speakers of the Geospatial World Forum 2019, Amsterdam, the Netherlands.

• The questionnaire for the study was finalized in collaboration by JRC and Geospatial Media and was subsequently added to an online survey portal for responses. (Finalized questionnaire enclosed in Annexure 1.) The questionnaire was circulated across Geospatial Media’s network via mailers, banner advertisements on geospatialworld.net portal, and through Geospatial Media’s extensive social media bandwidth.

• Geospatial Media reached out to the selected speakers at the Geospatial World Forum -2019 with the questionnaire and conducted one-on-one interviews with the stakeholders to capture their understanding, status, future plans and perspectives on AI and AI-based technologies (List of all stakeholders and interviews attached in Annexure 2.)

• Additional speakers from different sessions– such as the data science summit, location world, earth observation and construction and engineering at the Geospatial World Forum 2019 were identified and interviewed via online questionnaire/ and follow up email and interviews after the conference.

• In addition, stakeholders who due to unforeseen circumstances could not be interviewed at the Geospatial World Forum 2019 – were interviewed through email/skype/ and written interviews after the forum.

B. SECONDARY RESEARCH

• Available secondary literature on AI adoption in construction and engineering, earth observation, location analytics and business intelligence, and smart cities were reviewed and studied specifically to cite examples.

After the conclusion of the primary research and the secondary research, the data from the online survey questionnaire, and interviews were collated to draw out analysis and produce the findings in the report as presented in the following sections/chapters.

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RESPONDENTS PROFILE The study on the perspectives on the use of AI in the geospatial sector is based on an online survey questionnaire conducted with 92 respondents and also on the one-one-one interviews done with 30+ respondents (formal and informal) spread across geospatial companies serving the construction and engineering, earth observation, location analytics and business intelligence and smart cities sector. Of the sample size, sectoral wise respondents are as follows (corresponding to Figure 1):

• Construction and engineering – 19% of the respondents, i.e. 18 of 92 respondents, • Earth observation – 33% of the respondents, i.e. 30 of 92 respondents, • Location analytics and business intelligence - i.e. 26% of the respondents i.e. 24 of 92

respondents; and • Smart cities – 22% of the respondents i.e. 20 of 92 respondents

FIGURE 1: FOCUS INDUSTRY SEGMENTS OF 92 RESPONDENTS

On evaluating the regional profile of the respondents, the maximum number of respondents in this study, i.e. 32 respondents are from Europe; 24 respondents are from North America; 23 respondents are from Asia-Pacific; seven respondents are from Africa, four respondents are from Latin America and two respondents are from Middle East (Figure 2). FIGURE 2: REGIONAL PROFILE OF 92 RESPONDENTS

18, 19%

30, 33%24, 26%

20, 22% Construction and Engineering

Earth Observation

Location Analytics and Business Intelligence

Smart Cities

7, 8%

23, 25%

32, 35%

4, 4%

2, 2%

24, 26%AfricaAsia PacificEuropeLatin AmericaMiddle EastNorth America

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A further evaluation of the regional profile of respondents highlights that of the total 92 respondents – a total of 43 respondents are presently actively using AI as part of their solutions while the remaining 49 respondents are not using AI/or are investigating the use of AI/or are in the initial stages of implementation. Of the total 32 respondents from Europe – 65.6% of the respondents i.e. 21 respondents are presently using AI and AI-based technologies while 11 of the respondents are in the initial phases of implementation. Similarly, in Asia-Pacific, of the total 23 respondents – only five respondents state that they are offering AI as part of their solutions package – however, the remaining 18 respondents are currently investigating the use of AI for their solutions. Further on, even though there are only two respondents from Africa – both the respondents are presently not using AI in their solutions or otherwise (Figure 3). Our one-on-one interviews with the stakeholder’s highlights that while stakeholders in developing countries understand the benefits of utilizing AI and AI-based technologies – the level of adoption of AI is dependent on the technology and industry maturity of the companies in the region who are providing AI-based solutions. Since, most of the data-driven companies are located in the USA and Europe, the adoption of AI is also found to be a little higher in these countries – even if the AI research and development labs are outsourced to their Asia-Pacific subsidiaries. While there are a couple of success stories in India wherein AI is being used with geospatial, it is mostly the companies of North America and Europe who continue to lead the adoption of AI and AI-based technologies. Though the apprehension to use AI and AI-based technologies is found to be sub-optimal in the emerging technology markets – there is a growing awareness to use AI/ML technologies to address the pain points/ problems in the country. FIGURE 3: REGIONAL PROFILE OF AI ADOPTION ACCORDING TO ONLINE SURVEY (N= TOTAL RESPONDENTS)

Figure 4. shows an evaluation of respondents on the basis of their revenue profile. The graph highlights that 46%, i.e. 42 of the total 92 respondents are companies with revenue less than $10

57.1%

21.7%

65.6%50.0%

0.0%

45.8%

42.9%

78.3%

34.4%50.0%

100.0%

54.2%

Africa Asia Pacific Europe Latin America Middle East North America

Yes/Actively using AI=43 No/Investigating the use of AI=49

N = 7 N = 23 N = 32 N = 4 N = 2 N = 24

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million; 23%, i.e. 21 respondents belong to companies with annual revenue between $10 million to $100 million; 5% of the respondents, i.e. 5 respondents belong to companies with annual revenue between $100 billion to less than $1 billion; while approximately 16% of the respondents, i.e. 15 respondents belong to companies with their annual revenue being $1 billion or more. In the research, the concern is of nine companies who we found difficult to categorize on the basis of their annual revenues. It is to be noted, that these companies in all probability range less than $10 million and $10 million revenue profile. FIGURE 4: REVENUE PROFILE OF 92 RESPONDENT ORGANIZATIONS

Our interactions with the stakeholders show that while the higher revenue companies find it easier to make AI as part of their business solutions package; it is the smaller start-up organizations who develop AI-based solutions to address the pain points and problem areas in the four sectors. For instance, in a country like India, small start-ups with approximately 10-30 employees are providing AI-based solutions to address national issues and problems like pollution, land reclamation and land administration. However, it is important to note these companies are extremely small and are still finding ways to penetrate the industry verticals and enhance the market share of their technology. On the contrary, in technology ready regions such as North America and Europe both small and large enterprises are found to be at an experimental stage wherein AI and AI-based technologies such as ML/DL solutions are being developed to form a part of the wholesome solution package. The technology adoption in these enterprises is thus ingrained at a granular level in the workflow of the organization itself instead of only being recognized as part of the larger solution for customers and clients.

42, 46%

21, 23%

5, 5%

15, 16%

9, 10% Less than $10 million

$10 million to < $100 million

$100 million to < $1 billion

$1 billion or more

No response

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INTRODUCTION Theoretically, it is found that business enterprises worldwide are creating foundations within their organizational workflows to facilitate the adoption of Artificial Intelligence (AI) and AI-related technologies such as Machine Learning (ML), Deep Learning (DL) etc., to create data-driven value at a large scale. AI, as it is typically defined, is the ability of a machine to perform cognitive functions that a human can do such as perceiving, reasoning, learning and problem-solving. 1 Businesses, today, are found to integrate AI capabilities in their workflow processes and harness its benefits to improve efficiency, growth, productivity and innovation. Enterprise-wide automation and the abundance of big data sets enable AI-based applications make it possible for businesses to embed AI and AI-based technologies across varied sectors for predictive analysis and informed decision-making. While the adoption of AI in different industries is diverse – it is found that industry sectors where digitalization has already transformed the core aspects of business aspects – have a higher adoption potential of AI. Further, businesses operating in these sectors are also making higher investments in AI than before to generate greater value from using AI. Since AI is largely dependent on real-time and continuous flow of data – both spatial and non-spatial, in future, the geospatial industry will find it advantageous to adopt AI and AI-based technologies. With so much data being collected by the many sensors in the geospatial domain, AI will play a crucial role in augmenting the industry. AI AND GEOSPATIAL The synergistic confluence of AI and geospatial, also known as GeoAI, is important for the success of any industry sector looking to implement this technology. AI and AI based technologies empower workflow processes such as data/image processing, pattern recognition – by bringing in automation and avoiding the labor of doing everything manually. In earth observation segment, in particular, companies can put to use AI capabilities for the purpose of cloud detection, land cover change, change detection, to classify, analyze and process the petabytes of different satellite images from different sources, etc. With the introduction of AI, it is easier for the companies to present a structured knowledge structure which the human analysts find impossible. What was initially done manually, can be done quickly with machine learning and data processing. Using satellite imagery and AI based technologies – algorithmic models are developed for future prediction and hotspot analysis. Additionally, AI can be used to manage change detection in public space – especially the municipalities wherein when AI tools are applied on accurate geospatial databases to build footprints and detect changes in public space and traffic areas. HD Maps is another area where AI and AI-based applications and technologies are being developed. HD Map companies, for instance, TomTom is developing accurate street images – with lane divider and segmentations using deep learning techniques. They are also using AI to analyze GPS traces to provide insights to drivers regarding road closures, live traffic, traffic lights, speed cameras, among many other things. In the Geographic Information Software (GIS) technology segment, leading companies are providing in-built capabilities and functionalities

1 AI adoption advances but foundational barriers remain https://www.mckinsey.com/featured-insights/artificial-intelligence/ai-adoption-advances-but-foundational-barriers-remain

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inclusive of machine learning/deep learning, image recognition, object identification, predictive analysis and interpretation as part of their wholesome software package for customers to use it without having to separately ask for it. The industry too is recognizing the requirement and demand from the consumers to purchase an all-inclusive software solution instead of solutions which are available in silos. While there are few case studies of application of AI – especially in agriculture and healthcare sectors, the benefits of AI in geospatial and across the different sectors chosen for assessment is found to be typically low and is only expected to grow in the future. AI ADOPTION IN INDUSTRY SEGMENTS The adoption of AI is varied across different industry sectors. Theoretically, among the research, technology and media enthusiasts, there is a widespread excitement around the technology. The adoption of the technology will increase if this excitement translates into a meaningful strategy at an industry and an organization level. Gartner recently released a report based on a CIO survey (3000 respondents), wherein it emphasizes that AI adoption has tripled in the last year alone – with an estimated 37% of businesses now implementing AI and AI-based technologies in some form or other.2 While companies B today recognize the need for adopting immersive technologies like AI/ML or DL; there is very little evidence of use of geospatial with AI or GeoAI (except – earth observation). In the present, AI as a standalone technology forms the core of digitalization strategy of many businesses. The following sections present a theoretical overview of the role of AI technologies in the four identified sectors – construction and engineering, earth observation, location analytics and business intelligence, and smart cities. Further, the section on AI adoption at an organizational level (analysis from the online-survey and one-on-one interview) highlights the current and future adoption of AI and AI-based technologies. CONSTRUCTION AND ENGINEERING: While the construction and engineering industry is one of the most information-dependent sectors – it is also one of the least digitized sectors. Even though construction spending is approximately 13% of the world’s GDP; the productivity gains is still less than 1%. 3 Presently, the permeation of AI in the entire spectrum of the Architecture, Engineering and Construction (AEC) industry is found to be extremely low. While the potential of AI in the construction and engineering is vast – such that the use of AI and AI-based algorithms is possible across the construction workflow – i.e. planning, designing and engineering, construction and in the operations and maintenance phase of the project – the adoption numbers in this sector is found to be exceptionally low. It is the need of the hour that construction and engineering firms adopt AI in their workflow to transform the way in which buildings are designed, constructed and maintained. The geospatial industry catering to the AI industry understand the benefits of adopting AI solutions in in designing buildings through generative design, assigning priority to creation of

2 Gartner Survey shows 37% of Organizations have implemented AI in some form: https://www.gartner.com/en/newsroom/press-releases/2019-01-21-gartner-survey-shows-37-percent-of-organizations-have

3 Geospatial Market in AEC Industry Report by Geospatial Media and Communications

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3D models, etc., – however, the apprehension to adopt a new technology is found to be more. Companies realize that the use of AI algorithms can solve budget constraints in construction projects, analyze job sites, and help in judicious planning of the distribution of labor and machinery across jobs. Further, the construction industry can utilize AI-based solutions in analyzing data elements captured from mobile devices, drones, security sensors, etc., for accurate visualization to derive insights for the construction professionals to take informed decisions. AI based solutions in the operations and maintenance phase can be used to monitor the emergent problems in the structure of the building and provide solutions to mitigate any future risks. With the potential to alter business models of the construction industry, AI is foreseen to reduce inexpensive errors, make construction more efficient and ensure worksite safety. Investment in AI by construction and engineering firms, thus needs to be planned for the long term – as the benefits of the technology are going to be long term. EARTH OBSERVATION: In the earth observation sector, AI is being used to fully exploit and extract relevant information from the huge volumes of data captured and collected from the high number of high-resolution satellites and sensors. AI based solutions and algorithms - – also known as ‘transformational technology’ in the earth observation sector, provides for insightful data exploitation from satellite data and high-level information attraction for weather forecast, prediction and nowcasting. A case in point is the ɸ-Sat, or PhiSat, - with an AI technology which will fly on one of the two CubeSats of the FSSCat mission4. The mission shall entail collection of massive number of images of Earth – with and without cloud cover. The ɸ-Sat’s AI chip will not allow for the downloading of the not-so-perfect images back to the earth and would send only usable data for businesses and governments to use and derive qualitative information. Today, AI-driven satellite-based applications are garnering the attention of investors and venture capitalists (VCs) for a number of reasons which primarily includes ease of analysis from the enormous amount of data being produced from satellites and the reduction in the expenses with respect to imagery analytics and interpretation. AI tools and techniques helps the forecasters to get more out of the large volumes of data by improving accuracy and turning satellite observations/imagery into actionable forecasts. LOCATION ANALYTICS AND BUSINESS INTELLIGENCE: The location analytics and business intelligence industry are seen as one of the major users of AI and AI-based technologies. Location data is incredibly powerful and practical – and the predictive power of AI unlocks insights from incalculable data that is captured from multiple sources. Few of the companies which operate in the location analytics and business intelligence industry segment have already begun adopting AI solutions to uncover business insights and realize the undergoing digital transformation. Location analytics companies are using AI-based algorithms along with location and GIS data – across industries - ranging from health to retail, manufacturing to logistics and supply chain, etc.

4 First Satellite with AI ready for launch – ESA https://m.esa.int/Our_Activities/Observing_the_Earth/First_Earth_observation_satellite_with_AI_ready_for_launch

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Using AI, companies are able to broadly cull intelligence from the data and describe where things happen, why they happen, and how they can be improved. Because everything is happening somewhere, AI solutions ingest the massive amounts of the location data collected and generate insight to create and provide competitive advantage. Increasing number of geospatial companies in the location analytics and business intelligence sector are testing the capabilities of AI-based algorithms and recognizing the potential impact of AI for long-term business planning. Companies foresee the blend of GIS-driven location intelligence – powered by AI algorithms, in saving time and money while simultaneously increasing customer satisfaction. In the context of location intelligence- AI, IoT and big data technologies are being used in experimental projects to build and test highly complex but accurate algorithms to deploy a solution. Further, companies now aim to leverage real-time AI and location data to enhance their operations across the entire business workflow. One of the industries which is known to be using AI is the retail segment. Retailers are using real-time AI and location intelligence to optimize store inventory and supply chain, merchandize plans and the location of products in stock. The use of AI-based algorithms, as stated, helps the retailers to save money, build customer trust, achieve higher brand value and gain competitive advantage. These data-driven algorithms – along with location and AI data together authorize critical insights from data, recognize patterns on vast quantities of actionable spatial data to make data-driven decision making possible. SMART CITIES: Every smart city initiative in the world is aspiring to use AI and AI-powered vision systems to automate and improve the wide-range of city services and operations and maintenance. Cities have a wealth of data and possible data-sources available for AI pattern recognition technology to process and derive data-driven actionable insights. The potential of AI in smart city projects is immense as AI solutions can identify patterns from the collected sensor data and derive hidden correlations for predicting trends in the city. This means that AI systems can allow computers to identify the millions of elements of a city life inclusive of people, cars, accidents, traffic signals, garbage disposal, etc., and then use these elements for predictive analysis. In addition, in smart city projects, the AI-powered IoT enabled technology can track, process and evaluate how the different ‘elements’ of the city behave and respond to the existing city infrastructure and systems. The behavioral analysis by these systems enables city planners and decision-makers to understand how the city functions, responds to various changes, etc., such that decision-makers of the city are able to use these insights - create AI-based applications to enhance public transport and safety – by implementing smart traffic management solutions, smart parking, solutions while simultaneously improving health care, construction and engineering, urban planning and manufacturing.

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INVESTMENTS IN TECHNOLOGY AND HUMAN RESOURCE INVESTMENTS IN TECHNOLOGY: Geospatial companies aim to develop strategic capabilities within their organization to develop and utilize AI and AI-based technologies as part of their solution packages. Companies are building a series of applications to structure, model and create quality assurance models around the AI data models. High investments are being made to create a much deeper AI pipeline for data processing, linking and estimations surrounding geospatial data. Additionally, geospatial companies are also developing platforms for large scale work and are building supporting infrastructure for AI and AI-based technologies to function successfully; increasing their investments to augment the computing power of the organizational computers to ensure high-processing power of computers to enable the use of AI-based solutions; and are investing significant amount of time and resources in creating AI-based algorithms to generate intellectual properties. INVESTMENTS IN HUMAN RESOURCE: Our interactions with the stakeholders highlight that organizations foresee investments in human resource to be of more importance than investments in technology. Most of the smaller companies are strategically scheduling a significant proportion of their investments towards building human capabilities, to develop a community of learning and to bring forth more senior software engineering talent in the AI domain. Presently, especially in the geospatial companies – the human resource situation is such that engineers/data scientists have taken a learn as you go approach to develop AI-based solutions without having any expertise in the technology. Thus, companies have begun to invest in developing the capabilities of their human resources specifically in AI via grants and specific knowledge-based investments. Companies are making strategic investments in finding the right kind of people in a sector where talent is actually scarce. Bigger companies have their own start-up programs wherein investments are being made to develop qualified personnel – not only in the field of AI but also geospatial and vice-versa. The investment in human resource is thus a bigger part – as in a competitive and niche market as GeoAI it is difficult to find the right kind of people in research and development of these AI algorithms. POLICY READINESS AND STANDARDS DIGITAL READINESS: While evaluating the adoption of AI in the four industry sectors, the study found it appropriate to draw a correlation between the digital readiness of the industry segment and the adoption of AI technologies in the sector. Of the four industry sectors, the digital readiness of the earth observation sector is the highest, followed by the location analytics and business intelligence sector. Given that the earth observation industry is found to be one of the core geospatial industry segments, the companies operating in this sector have taken strategic steps to adopt and embed AI and AI-based technologies and algorithms as part of their workflows. In contrast, as mentioned above, the digital readiness of the construction and engineering segment is relatively low and thus the adoption of AI based solutions in construction projects across the globe is poor. Construction companies, especially in developing parts of the world, are struggling

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to adopt core construction related software for implementation i.e. BIM –, the adoption of AI which is an add on to BIM is found to be significantly lower. POLICY READINESS is also found to be a crucial aspect in enhancing the adoption, and in encouraging the implementation of AI based solutions across sectors. With respect to AI adoption – policies are found to be critical to drive the adoption of AI based solutions in different industry verticals. Since, AI is largely dependent on big data – a lot of data needs to be placed in the free domain thus proving open data policies to be important for the adoption of AI. While there exists, a strategic challenge given the aspect of privacy –the geospatial industry is of the opinion that certain select pieces of information should be made open specially when it is collected by the government agencies and people are paying taxes to access the same. Open data policies shall, thus, enable more companies to experiment and innovate in the field of AI as data availability continues to be a core challenge in this field. Today, because of lack of data availability, companies have to go to private firms who make high-resolution data available at higher costs; thus, making AI-based technology research and development expensive. By making data available in open domains, government authorities can employ, create and train a lot many local models, publish those models to make them available to the users and provide immediate solutions to problems. The geospatial industry is of the opinion that if government’s take the initiative to create and develop AI models with the help of the industry, it will be easier for the users to implement the models without having to worry about transparency, security, creating models from inaccurate data, and increasing costs of investment in human resource and technology. Note: In a recent turn of events, the USA has decided to restrict exports of certain AI programs under the Export Control Reform Act (ECRA) specifically, for neural networks (a key component in Machine Learning) to discover points of interest in geospatial imagery. 5 The impact of the ECRA on the geosaptial companies operating in the field of AI for satellite imagery – is thus, yet to be seen. It is to be noted, that in our latest interactions with the companies in the USA; the industry – especially the small start-ups are now wary of expanding their AI businesses outside the country. Additionally, standardization organizations such as Open Geospatial Consortium (OGC) are actively tracking the growing AI-technology momentum. Geospatial companies’ part of the standard organizations is collaborating to discuss and establish the standards for geospatial and AI technologies or GeoAI. Keeping this perspective, a group called Geo Pulse has been set up by GeoAI organizations. Since AI is expected to pervasive across the geospatial industry; the industry claims standards to be important for easy access to high-resolutions standardized datasets for AI algorithms. Every organization is keen on developing its own unique AI algorithm to bring a symmetry across the data frame and for this purpose standards are imperative. Standards may not be the end goal; but currently it is one of the primary goals of the geospatial organizations with respect to AI.

5 https://www.theverge.com/2020/1/5/21050508/us-export-ban-ai-software-china-geospatial-analysis

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AI ADOPTION AT AN ORGANIZATIONAL LEVEL CURRENT ADOPTION

The current adoption of AI at an organization level is derived from the 92 responses received on the online questionnaire (Questionnaire attached in Annexure 1). The study finds that while the number of respondents is limited – the increasing trend of adoption of AI in the geospatial industry has only been seen over the past two-three years. Additionally, companies which have implemented AI in their organizations; have yet not implemented the technology across the entire spectrum of the organization but only for specific projects or across one department. Also implemented as a pilot technology in projects, for full implementation, companies are awaiting to rationalize the benefits they derive at a more granular level. Figure 5 categorizes the AI adoption at an organizational level as – (1) organizations actively using AI in their organizations and moving towards enterprise-wide adoption (43 respondents) and (2) not using AI/initial stages of AI implementation (49 respondents). Our analysis finds: 43 of the total 92 organizations are actively using AI in their organizational workflows. Of these 43 organizations – 14 of the organizations have already implemented AI enterprise-wide, while seven of the respondents are found to be planning for enterprise-wide implementation after being satisfied with the initial value-proposition, results and findings. Additionally, seven of the 43 organizations are found to be using AI in multiple departments while 13 of the remaining respondents are found to be in the planning phase of AI implementation across different department groups. A cross over-analysis shows that most of the respondents in this category are from the earth observation industry segment wherein the use of AI and AI based technologies is noticeable and found to be higher than the other sectors taken under observation. Simultaneously, many location analytics and business intelligence companies have begun to utilize the combined power of AI+GIS (GeoAI) inclusive of location data to derive insights, predict trends, and define business plans and strategies (Figure 5). Subsequently, 49 of the total 92 organizations are found to be in the second category of AI adoption. 20 of the 49 organizations are found to be investigating the use of AI – i.e. these organizations do not use AI presently but are exploring the value-proposition of using AI and the probable benefits the technology has to offer in their area of work. Since the technology is still believed to be in the infancy stage, its application experimental, there still exists substantial apprehensions on the use of the technology. Of the 49 organization, 29 organizations are found to be in the initial stages of implementation of AI in pilot projects. Most of the companies in this segment are those catering to the construction and engineering and the smart cities sector. Most of the geospatial companies developing solutions in the construction and engineering sector are found to be contemplating the use of AI in their workflows since the sector is one of the least digitized sectors and the utilization of any emerging technology has a significantly slower adoption rate. While the stakeholders interviewed for the purpose of this study are upbeat of the possibilities of using AI in the construction and engineering sector – the industry is of the opinion that AI adoption in these sectors is going to significantly more time. On one-on-one interaction with the

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geospatial industry, it is found that AI as a technology is not exclusively demanded by the users, but it is provided as an embedded capability in the wholesome solution package offered by the industry. However, because AI is still considered to be a new technology, the adoption of it is being investigated for all industry domains and algorithms are being tested in pilot projects to understand the return on investment and long-term benefits (Figure 5).

FIGURE 5: CURRENT LEVEL OF MATURITY OF AI AT AN ORGANIZATIONAL LEVEL (TOTAL RESPONDENTS=92 RESPONDENTS)

At an organizational level, validating our findings and analysis for AI adoption in industry segments, on a mutually non-exclusive question, stakeholders specify that of all AI technologies – the two traditional AI technologies, i.e. machine/deep learning and image recognition/computer vision are found to be most relevant by all stakeholders at the organizational level. Sound recognition and processing, NLP and robotics are AI technologies found to be gradually gaining traction in the industry sectors and in organizations – even though their relevance in the industry sectors is still being investigated. Respondents who are using AI technologies implement the traditional neural networks specially to process imageries and vector data available in the market - along with statistical analysis to build better compression platforms for identified problems. In terms of industry vertical adoption – machine learning/deep learning and image recognition/computer vision are two AI-based technologies which are being implemented extensively in the earth observation and the location analytics and business intelligence sectors. Furthermore, these are also the two technologies which are believed to be relevant in the construction and engineering and smart city segments in the future. Robotics, especially construction robotics which is a forward-looking technology in the construction and engineering sector is also expected to gain foothold in terms of adoption.

20

29

13

9

7

14

0 5 10 15 20 25 30 35

Investigating the use of AI

In initial stages of implementation in pilot projects

Planning to expand to other departments

Using in multiple departments

Planning for enterprise-wide implementation

Employed enterprise-wide

Not

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=43)

15

On a mutually non-exclusive dataset, respondents state that Machine/Deep Learning and Image Recognition/Computer Vision is found to be most relevant at the organization level followed by Natural Language Processing (NLP). Sound Recognition and Processing and Robots are presently the least relevant AI technology at the organizational level (Figure 6). FIGURE 6: AI TECHNOLOGY FOUND TO BE MOST RELEVANT AT THE ORGANIZATIONAL LEVEL (TOTAL RESPONDENTS=43 RESPONDENTS) *Mutually Non-Exclusive Data Sets

It is almost to calculate the exact return on investment of investing in AI technology and therefore, stakeholders are found to be assessing their return on investment / and the success of AI adoption on qualitative aspects instead of quantitative data. On a mutually non-exclusive dataset, the analysis of qualitative aspect highlights that 42 respondents have witnessed higher productivity gains because of AI implementation across the organizational workflows; while 35+ respondents (each) believe that the success of AI can be judged by the fact that there is better production and operation analytics, improvement in work quality, better data archiving and data mining, and significant project cost reduction (Figure 7). FIGURE 7: MEASURING SUCCESS OF AI ADOPTION (TOTAL RESPONDENTS = =43)- *Mutually Non-Exclusive Data Sets

7

41

7

40

21

0 5 10 15 20 25 30 35 40 45

Robots

Machine/Deep Learning

Sound Recognition and Processing

Image Recognition/Computer Vision

Natural Language Processing

4236

3335

3230

2822

118

0 5 10 15 20 25 30 35 40 45

Productivity GainsImprovement in work qualityFinal project cost reduction

Better data archiving and data miningFaster product development

Better production and operation analyticsImproveed customer service/interface

New market segment penetration/serviceBetter sales analyticsReduction of HR cost

16

Image recognition is the most commonly identified AI technology put in use by the earth observation sector to identify features of the imagery and then understand the changes from over time. Today, there exists many open source libraries for carrying out image recognition – however using these libraries and applying spatial analysis on them is found to be quite challenging because traditional image processing identifies only static things. To address this challenge, companies are developing image processing solutions which take a lot of existing algorithms and apply them to problems to derive solutions. On the other hand, there are many geospatial organizations who are still looking at traditional neural networks and traditional relational models and statistical analysis. In context, machine/deep learning is one of the only AI-based technology that has the ability to handle greater volumes of data. Additionally, machine learning is being used relatively more when compared to other AI technologies as clients demand analysis from specific information from the data sources available. For instance, if a user would like to identify solar panels on the top of the houses, it can be captured as a drone raster imager or on a satellite imagery – machine learning makes it possible. Since most of the geospatial organizations have to work with large volumes of data and provide customers with specific solutions – they find machine/deep learning to the most useful AI-technology. Recognizing AI and especially ML/DL there are a number of AI based case studies wherein geospatial companies are encompassing AI and AI based tools in their solution packages – to help in the delivery of better products to a larger market segment. While the users of geospatial solutions are found to not demand AI as a technology per say; they are demanding for specific solutions rather than raw data. AI makes it possible to develop a synergy between geospatial and AI (GeoAI) to process petabytes of data, develop specialized solutions to realize significant economic benefit for the geospatial industry and boost sales and profits. Further, the use of AI also helps drive the socio-economic factor. For instance, for an earth observation company working in tandem with the government bodies, it is easy to derive incredible insights from the satellite imagery –how is it changing – for good or the bad – what are the environmental issues, etc., by providing wealth of knowledge to all key stakeholders. GeoAI has the potential of enhancing the decisional approaches and deliver the already existing products and services in a better package. High-resolution and real-time spatial data augmented by AI leads to development of good products and applications to derive real benefits from it. Further, companies are looking to deploy AI to identify and solve algorithm failures. In software companies – failures generate a log of information which is a physical sheet of information. Using AI - companies can mine these logs and predict where things can fail in future so that they can proactively deal with these kinds of situations. In addition, the use of AI also enables better application services to customers. AI and AI-based technologies are able to conduct an accurate predictive analysis on what the customer will do, what will be their spending – and help the organizations to target those clients accordingly. AI, thus, provides stakeholder with the ability of decision-making based on the petabytes of data that has been collected over the years.

17

FUTURE ADOPTION OF AI The above analysis shows the current adoption and implementation of AI and AI-based technologies to be at a nascent stage with companies experimenting and implementing with AI algorithms in part of their projects/or pilot projects even if not in the entire project spectrum. At present, AI is being tried in pilot projects and the benefits of deploying AI based algorithms is being investigated. The future outlook of AI adoption thus shows a positive trend. In the earlier section, Figure 5 highlighted that 43 respondents are presently adopting AI, while 49 respondents are not. The evaluation of the future adoption of AI brings forth a positive transition wherein 17 respondents who are not using AI presently – would begin using AI and AI based technologies as part of their solution workflows. Of the 49 respondents who were not using AI before, 32 respondents still don’t foresee using AI at all in the future (Figure 8). The 43 respondents who were using AI before are very positive about the outlook of AI and AI-based technologies in their workflows and all of them are going to continue using AI in the future also. FIGURE 8: FUTURE ADOPTION OF AI (TOTAL RESPONDENTS = 92)

The 17 respondents who have shown willingness to adopt AI technologies in the future – state that they are motivated to adopt AI because their research and other case studies brings forth the major benefits that can be derived from the use of AI and AI based technologies – such as substantial increase in productivity and improvement in operational efficiencies. The respondents state that they are motivated to use AI because they foresee greater timesaving’s by the automated process to be an important benefit. Few of the respondents are positive because they foresee AI will help to optimize the utilization of resources, save costs, improve customer experience and make informed, faster and better decisions. Companies state that using AI, they would be able to predict customer behavior in terms of spending patterns, purchasing behavior, etc.; leading to targeted advertisement patterns, designing sales strategy for higher efficiency and productivity (Figure 9). Upto 13 of the 43 respondents who are currently using AI and plan to do

32

43

17

0 5 10 15 20 25 30 35 40 45 50

No - we don't use AI currently and we won't beusing AI in the future

We are currently using AI and are most likely tocontinue using AI in our solutions

We will begun using AI in the future

No

- AI

adop

tion

inth

e fu

ture

Yes

- AI a

dopt

ion

in th

efu

ture

18

so in the future are of the opinion that the increasing productivity and efficiency is the most motivating factor for them to expand AI capabilities across their organization. FIGURE 9: MOTIVATING FACTORS FOR COMPANIES TO EXPAND USE OF AI IN THE FUTURE (TOTAL RESPONDENTS = 60)

Validating the survey findings, our interaction with stakeholders – especially in the construction and engineering segment, also highlights that while the adoption of AI is presently negligible in the sector, in future companies are willing to invest in AI technologies because of the significant time and cost savings the technology has the potential to bring to the workflows by way of optimum resource utilization and by automation of processes. Secondly, stakeholders who are currently using AI also state that because of present use of AI they foresee the reduction in human error to be one of the reasons to expand AI adoption enterprise-wide in their organizations in the future. One example that was cited by an AI solution provider for smart city projects is that maintaining an inventory of all information, object classification and image recognition can be a challenge if done manually and at times leads to multiple errors. Additionally, since it is a labor-intensive job – it involves substantial amount of time and costs (and re-checks). Stakeholders comply that they want to adopt AI and AI-based technologies to save time and cost and reduce the possibility of human error. The 22% of the respondents i.e. 7 respondents of the 32 respondents who are unwilling to adopt AI technologies in the present and in the future, state that they are restricted by a knowledge gap when it comes to the adoption of AI (Figure 10). These stakeholder’s state that the unavailability of reliable consultants and vendors to guide and/or implement the technology is one of the most important determinants for them to not consider investing in AI technology at the present. Believing this field to be niche, the respondents have developed a pessimistic approach towards

13

76

56 6

0

4

2

43

0

2 2

0

2

4

6

8

10

12

14

Increaseproductivity

andoperationalefficiencies

Cost savingsby optimizing

resourceutilization

Save time byautomatingprocesses

Make fasterbusinessdecisions

Avoid 'humanerror'

Improvecustomer

experience

Others(pleasespecify)

Stakeholders currently using AI and who shall continue using AI in future (N=43)New AI users - in future (N=17)

19

the technology, thus causing a detrimental effect to the adoption of AI than positive. Further, stakeholders also negate the notion that AI brings with it any substantial return on investment if deployed. In addition, approximately 19% of the stakeholder’s i.e. 6 respondents (each) highlight that the high costs associated with the seemingly expensive technology and the lack of trained human resource is more of a ‘money trap’ more than an investment. These stakeholders are of the opinion that the existing knowledge and skill gap will result in high costs, thus, making AI to be an expensive tool for them (Figure 10). FIGURE 10: REASONS FOR COMPANIES NOT WANTING TO ADOPT AI IN THE FUTURE (TOTAL RESPONDENTS: 32)

Our one-on-one interaction with the stakeholder highlights that investment in human resource is much more than the technology itself. The market for AI is essentially a competitive market and it is difficult for companies to find skilled and qualified personnel in research and development. The human resource cost and the additional training cost – makes the adoption of AI expensive for companies. Approximately 41% of the respondents (lack of trained HR + unavailability of reliable consultants to guide or implement the technology) i.e. there are 13 respondents who are unwilling to adopt AI technology significantly believe human personnel – as human resource and as a technology vendor to be the greatest challenge in the adoption of technology. CHALLENGES IN ADOPTION OF AI IN GEOSPATIAL While the benefits of adopting geospatial and AI technologies together are significantly more – there are significant challenges associated with the same. One of the principal challenges that the industry continues to face is related to understanding what an algorithm is, how it is formulated and how does it work. Governments all over the world are formulating necessary policy frameworks to understand the complexities and sensitivities associated with AI algorithms and if these technologies can lead to the possibility of the human race being ruled by machines where AI will take all decisions and the human race would be unaware of the potential of AI algorithms to control us. For instance, the recent example of Facebook wherein AI algorithms can track every

6, 19%

6, 19%

2, 6%6, 19%

7, 22%

5, 15%

High costs

Lack of trained HR

Lack of clarity on government policies andlaws

Don't see value in using AI at present

Unavailability of reliable consultants toguide and/or implement the technology

Other reasons

20

move of an individual and customize an individuals’ newsfeed accordingly has made the policy makers vary of the technology. Another common challenge the geosaptial industry faces in the adoption of AI is the availability of huge volumes of data and the regular and real-time streams of data that can be used to train the AI algorithms. Today, there are petabytes of data collected day in and day out, however, the question remains on who owns the data and what is the quality of data made available for these algorithms to work on. Most of the large HD Mapping companies are at an advantage here as they are able to stimulate and use their own datasets to test the algorithms and outcomes. The lack of freely available high-resolution datasets restricts the smaller companies to model AI-based algorithms. Thus, many of the smaller companies advocate for open data policies albeit keeping a check on privacy issues – so that they are able to develop AI solutions/algorithms. Many data scientists also feel restricted by the privacy and anonymization process because they are unable to exploit the data to its full potential – which sometimes results in unsuccessful algorithms. Another important challenge is that of the human resource. As we saw in Figure 10, lack of trained HR + unavailability of reliable consultants to guide or implement the technology is one of the main challenges why the naysayers don’t want to adopt AI. Since expertise in AI and AI-based technologies is a fairly new skill, the challenge is much more profound than any other sector. It is important to note that the geospatial industry in itself has been struggling with human resource crunch for the longest time – however, with an added AI capability, organizations are expressing the need for human capital expert in both geospatial and AI. Organizations are thus struggling to find skilled engineers who are both geospatial experts and AI/ML/DL experts. It is only in last few years that the availability of data scientist skills has risen at any reasonable scale. However, to keep up with the pace of technology advancements, many geospatial companies, are now taking initiatives to train their geospatially skilled human resource in AI and AI skilled human resource in geospatial. Geospatial organizations have also taken upon themselves to set up innovation and incubation centres, research and development labs, summer school programs, among many other things to address this specific challenge. A critical challenge faced by the geospatial industries in full adoption of AI is the knowledge gap. Presently, there is an evident gap between the AI-language as used by data scientists and as understood by the business managers. The business managers who have to formulate and implement strategic business decisions regarding the sale of the solution package often find it difficult to understand the technical language and expertise of the data scientists. It is, therefore suggested that it is important to develop a common vocabulary so that both the data scientists and the business managers are able to understand each other’s vocabulary and each other’s solutions ecosystem. The industry strongly believes that once this gap is bridged, there will be a wider adoption of AI across varied sectors.

21

CONSTRUCTION AND ENGINEERING The construction industry is known to be one of the least digitalized sectors and therefore, the adoption of technologies to improve productivity, efficiency and revolutionize the industry has progressed quite slowly. The varied uses of AI and AI-based algorithms across different phases of construction and engineering workflow has opened up a whole new universe of possibilities for the traditionally and technologically slow old-fashioned construction industry. With the global construction market set to grow as big as US$ 12.9 trillion by 2022, propelled predominantly by countries like USA, China and India, the number of opportunities pose to be endless. In recent times, construction and engineering firms have begun deliberation on the importance of using AI and AI-based technologies to transform their work processes from construction to ‘artificial construction’. Artificial construction leads to real time virtual reality construction models which further can lead to a reduction in errors. AI and AI-based technologies can be implemented across the construction workflow - from the planning stage, to designing and engineering, construction and further onto operations and maintenance. Today, Construction Technology (ConTech) start-ups are looking to develop unique value propositions – such as providing construction planning platforms with an AI engine to optimize the construction planning and designing and engineering processes. The startups especially are looking to develop AI based solutions to digitize the construction planning process to optimize construction project plan. Further, larger companies like Autodesk Inc. and Bentley Systems, have already taken step towards developing AI based technology solutions– to provide specialized AI based solutions for construction images and data i.e. BIM and geospatial data in a common data environment. In terms of benefit, AI – associated with machine learning is expected to mitigate risks, provide worksite safety and accelerate decision making process and eliminate errors. At the same time one of the most interesting technology of AI – Robotics – is beginning to proliferate the construction sector. Today, construction robotics are ushering in a new age in the workflow paradigm taking over dangerous and highly competitive tasks, thus, improving productivity and efficiency. One of the best examples is that of the recent collaboration of Trimble, Hilti, and Boston Dynamics for the Spot Robot platform to support the dynamic nature of the construction environment. Primarily, in the construction and engineering segment AI can be used to reinforce the learning process which means algorithms can be written to conduct trial and error studies without any kind of risk. AI in this case, will greatly contribute in successfully executing an action plan for planning, scheduling of tasks, testing viability of solutions as well as effectiveness of materials. A case in point - Autodesk has come up with an updated software called BIM 360 Project IQ which brings together connected data and machine learning (AI-based technology) to forecast and prioritize high-risk issues and provide invaluable insights to the construction managers to resolve their challenges. Further, AI can also be implemented to improve supply chain coordination which in turn will help in cost management to control and optimize the flow of money. Theoretically, it has been well established that AI can play a major role in decreasing construction cost, the proposition of using AI in construction industry in the future seems to be unavoidable and well received by all the stakeholders.

22

Presently, the adoption of AI in construction and engineering industry is at an experimental stage and if there is any implementation, it is at a slow place. In this sector, particularly, whatever implementation is seen in the design process and not in the construction and engineering workflow. Because construction is more analogue than the designing and planning part of the construction lifecycle, the application of AI in this subset will take more time – and the respondents estimate it is going to take approximately five years for complete lifecycle implementation. In the construction and engineering sector, there exists a massive knowledge gap and apprehension to adopt AI-based solutions. Since, the AEC industry operates on low margins with high risks and deeper levels of outsourcing and sub-contracting - there exists a need to inform and create awareness about AI in this sector. The responses that we have received via our online questionnaire and our interaction with the 18 stakeholders who are either presently using or are investigating the use of AI and AI-based technologies in the construction and engineering sector, shows that of the total 18 respondents, five respondents are presently providing AI solutions for construction and engineering projects while the remaining 13 respondents denied using/or providing AI, and AI-based solutions for construction projects presently. The five companies who are currently using AI technologies as part of their solution package state that the adoption is at a very nascent stage – almost such that the adoption of AI technology in construction and engineering is hardly noticeable (Figure 11). FIGURE 11: THE CURRENT USE OF AI TECHNOLOGIES IN CONSTRUCTION AND ENGINEERING PROJECTS (TOTAL RESPONDENTS IN CONSTRUCTION & ENGINEERING SECTOR: 18)

While the sample for analysis is low – the five respondents who are using geospatial and AI in the construction and engineering sector state that ML/DL paves way for Artificial Construction. and the market is optimistic that AI based solutions can be used in the production of robotic arms through simulation which can help in prefabrication of materials to perform maintenance tasks productively. Research finds that ML is the backbone of construction which helps to explore all possible design alternatives and run tests to investigate which design is better and identify any possible clash detection in terms of the 3D model and the original design of the building. Further, potential is seen in construction companies to use AI and ML techniques to draw and improve plans for distribution of labor and machinery across a varied spectrum of jobs. On the other hand, construction robotics can help project owners to keep a check on the job progress and the location

5, 28%

13, 72%

Yes

No

23

of workers and equipment, thus, enabling project managers to identify lapses in operation and identify tasks which need additional labor and/or equipment. Experts and respondents foresee construction robots to be more intelligent and autonomous in days to come owing to the stark advancement in AI. Further, ML/DL is foreseen to and improve 3D models of Building Information Modeling (BIM) created using drone imagery and other sources to create an opportunity to cross-check for errors as well as reduce decision making time drastically. Our findings from the online survey shows that the five geospatial companies who are putting to use AI-based algorithms as part of their technology toolkit especially in road and tunnel projects, and buildings and dams’ projects (Figure 12). FIGURE 12: KIND OF AI TECHNOLOGIES IMPLEMENTED IN CONSTRUCTION & ENGINEERING PROJECTS (TOTAL RESPONDENTS USING AI IN CONSTRUCTION AND ENGINEERING SECTOR = 5)

Even though the sample size for the respondents for this segment is low; and even the respondents who are using AI is extremely low - ML/DL is the preferred choice of AI technology in construction and engineering projects across sub-sectors like roads, tunnels, buildings, dams and utilities sector. Seen as the next frontier in construction technology, ML is described as a ‘smart assistant’, to help construction project managers identify the most critical risks related to quality control and construction safety. In construction projects, there are multitude of changes and open issues, hundreds of RFIs and numerous construction orders which respondents believe can be solved by machine learning, saving project time and cost. The five users are of the opinion that both ML and DL helps project managers to make decisions and calculate associated risks. In construction projects, ML/DL is used to tag and organize project documents, pilot drones, identify conflicts or missing materials, and automatically tag individual projects. Thus, in construction, the respondents are using ML/DL to predict and mitigate risks, avoid negative reactions by high-risk subcontractors, high risk issues and potential safety concerns by identifying real-time data and classifying actionable items to bring a revolution to the job sites.

3

2

1

5

2 2 2

1

2 2 2 2

1 1

5 5

2 2

4

3

2

1 1

0

1

2

3

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6

Roads Tunnels Bridges Airports Railroads Facilities Buildings Dams Utilities

Num

ber o

f res

pond

ents

usi

ng A

I in

cons

truct

ion

and

engi

neer

ing

proj

ects

Natural Language Processing Image Recognition/Computer VisionSound Recognition and Processing Machine/Deep LearningRobots

24

In addition to ML/DL – the five respondents also emphasize on NLP to be the second AI-based technology used by the construction project managers to automatically provide project status updates – making it consistent and objective. The respondents also state that construction companies too have begun to identify the role of NLP in realizing the value of big data collated from BIM. Research studies brings forth that NLP helps analyze the data and text and its integration with the BIM software in the common data environment (CDE). This in return eases the decision-making process and mitigates future risks. AI based technology - image recognition/computer vision enables easy object and image recognition and classification. While the companies may not be using AI presently, companies are looking at deploying image recognition/computer vision software in construction worksites where they are able to identify construction workers, material objects and avoid any high-risk situation in the future. While due to small sample size, it is not seen in our analysis, however, today, there are many case studies available which showcase how robotics – another branch of AI is being applied to pre-fabrication techniques and operations and maintenance phase of the construction lifecycle.

Complying with the analysis found Figure 12, the research study highlights that ML/DL is a sort after AI technology across the construction lifecycle – plan, design, build and operate. Of the five respondents who are currently using AI, on an average four respondents confirm to the use of AI and AI based technologies across all phases of the construction lifecycle. Following ML/DL is the image recognition/computer vision technology which is being put to use across the construction workflow. The respondents are of the opinion that while ML/DL helps in identifying and processing real-time construction site information especially related to project documentation, construction site hazards and risks; image recognition/ computer vision plays a critical role in worker identification and material classification –ensuring optimization of resources. The stakeholders who have used both ML/DL and image recognition and computer vision in their software solutions for construction and engineering sector, find it generates substantial inferences for the decision-making process. The stakeholders have also used ML/DL to process construction documents, identify conflicts, objectify missing materials, automate the construction workflow, analyze individual’s responsibilities – and find it to be useful for the construction phase of the AEC lifecycle

FIGURE 13: IMPLEMENTATION OF AI TECHNOLOGY ACROSS THE CONSTRUCTION AND ENGINEERING

LIFECYCLE (TOTAL RESPONDENTS USING AI IN CONSTRUCTION AND ENGINEERING SECTOR = 5)

1 1 1 12

32 2

43

43

1

012345

Plan Design Build OperateNum

ber o

f res

pond

ents

usi

ng

AI in

con

stru

ctio

n an

d en

gine

erin

g pr

ojec

ts

Natural Language Processing Image Recognition/Computer VisionSound Recognition and Processing Machine/Deep LearningRobots

25

AI and AI-based algorithms are being used to empower the varied applications of geospatial technologies. Understanding that the construction and engineering industry is undergoing a major seismic transformation – the synchronization of AI, BIM and geospatial technology (GIS) is foreseen to be crucial for the digitalization of the construction sector. The five respondents who are using AI, highlight that implementing AI based technologies in conjunction with GNSS/GPS has helped them with the development of an autonomous quality control system – assisting with construction on-site activities. As more and more construction companies realize the benefits of using AI and GPS in combination, there would be less risks, costs and increase in overall productivity of the segment. These integrated solutions in construction projects makes for improved asset-tracking systems thus enhancing efficiency, safety and maintenance procedures (Figure 14). FIGURE 14: AI USE WITH GEOSPATIAL TECHNOLOGIES IN CONSTRUCTION AND ENGINEERING (TOTAL RESPONDENTS USING AI IN CONSTRUCTION AND ENGINEERING SECTOR = 5)

Of the five respondents using AI – four of the respondents are using AI with scanning. The 3D images rendered from aerial LiDAR, LiDAR, and scanning technologies can be rendered into 3D building models by the use of AI in construction and engineering projects. Thus, AI and AI based deep convolutional neural networks can be used to automatically generate image processing coefficients in real-time i.e. as and when the data is generated by the geospatial data sources. In Netherlands for instance, geospatial technologies are being used to collect all data related to road – inclusive of all kinds of assets on the roads – inclusive of road signs, road markings, or even damages on the road itself. While many of the government stakeholders extract this information manually, a Netherlands based start-up has optimized the entire process using AI technologies – computer vision, ML/DL – wherein the AI algorithms are able to detect upto 80-90% of the objects on the road – thus optimizing the operations and maintenance phase of road construction. This has resulted in time and cost savings improving productivity and efficiency.

5

4

2

4

0 1 2 3 4 5 6

GNSS and Positioning

GIS and Spatial Analytics

Earth Observation

Scanning

Number of respondents using AI in construction and engineering projects

26

CONCLUSION The adoption of AI in the construction and engineering sector is at a nascent stage because this sector is the least digitized. Additionally, lack of government mandate and lack of user awareness – is one of the major factors why construction companies are not looking to adopt these technologies. While people in the sector are aware of AI– there is a limited understanding of its implementation. The adoption of technology is less in construction sector as it is, and with the pace of technology advancement it is difficult for the industry to keep up with these technology trends. While the construction companies are now beginning to understand the higher levels of automation AI can bring to the sector; there exists a need to develop a strategic roadmap within the organization to adopt AI and such related technologies. One of the striking features of AI adoption is that it varies regionally - i.e. the awareness of AI and AI based technologies is found to be significantly higher in the North America region, followed by Europe and almost negligible in Asia-Pacific (excluding Singapore). While the user segment are found to be increasingly vary of the use of technology; it is the technology providers – of BIM and geospatial – who are looking to imbibing AI into their software tools to automate the use of the software, reduce the risk, cross-check for errors, reduce project cost and time and improve the decision-making process across the construction lifecycle. Today, AI and AI based algorithms can be used in synchronization with geospatial technologies – that is, the GPS/GNSS and Positioning based systems in construction and engineering projects. Since AI is capable of being integrated with both GIS and BIM – the technology can enhance the efficiency and productivity of the entire construction workflow – plan, design, build, and operate. It is time the construction industry realizes that AI is critical for the wholesome development of the construction industry to deliver value to society into a livable world.

27

EARTH OBSERVATION Today, data scientists are looking at the earth observation sector optimistically. With petabytes of data generated from satellite imagery every single day, AI and AI-based technologies is being used by scientists for data analysis and to derive new insights. Given that earth observation is one of the four core geospatial technology segments – it is also a source of huge number of data in form of satellite imagery. The raw data collected and collated from the satellites have to be cleaned, processed and skimmed through for identification of patterns, predictive analysis/change detection, among many other things. AI and AI based technologies – especially implementation of ML/DL is found to make this task easier. One of the recent and strategic examples of AI implementation in the earth observation sector is that of the PhiSat being launched by the European Space Agency (ESA). ESA is poised to launch the first European satellite equipped with AI to improve the efficiency of earth observation data collected on board. The satellite, PhiSat will be the first of its kind to host AI technology. 6 This space mission among many other showcases the proliferation of AI set to transform the market landscape of the earth observation. Among, the start-ups and the big players, a trend is being observed of leveraging ML and other forms of AI to extract valuable insights from the growing abundance of satellite imagery. Thus, there is an increasing demand within the earth observation community for satellite-data applications which utilizes AI for processing the exponential amount of data produced by the satellites all over the world. Since companies are beginning to identify that the traditional method of imagery analytics and interpretation is time-consuming and expensive, they foresee AI-drive applications to play a much significant role in this transition. Further, as the earth observation market witnesses a strategic shift of the user demand from imagery to insightful analytics - AI, ML/DL plays a crucial role in providing change detection analysis, sector segmentation, and classification of buildings, ships, vehicles, and persons; accurate classification of land use/land cover, and settlement types - in the multitudes of satellite data collected. Further, AI algorithms are being used for change detection and time series analysis which leads to accurate imagery analysis and interpretation. Today, leading earth observation companies are developing cloud-based multi-source data platform which are powered by AI-ML to unlock the value of growing earth observation data. Such trends can be seen in the on-going mergers & acquisitions - for instance, DigitalGlobe one of the leading companies in earth observation business, has acquired two companies, working on analyzing satellite imagery with ML algorithms - Radiant Group (an established EO business working with US intelligence customers) was bought by the firm for $140M. Timbr.io – data science startup made some interesting work with satellite imagery – was also brought up by DigitalGlobe for an undisclosed amount 7. Further, AI-driven earth observation start-ups raised US$96 million – three times more than in 2016 – highlighting the focus of earth observation companies to the field of AI. The market is set to witness a rise in dynamic earth observation platforms which will enable the users to seamlessly adapt to an array of AI models and data availability.

6 https://www.esa.int/Our_Activities/Observing_the_Earth/First_Earth_observation_satellite_with_AI_ready_for_launch 7 https://emerj.com/ai-sector-overviews/ai-applications-for-satellite-imagery-and-data/

28

Our interactions with the stakeholders and responses on the online questionnaire, highlights that about 73% i.e. 22 of the total respondents of the earth observation sector i.e. 30 - are using AI technologies in earth observation, while the remaining 8 respondents denied using AI presently as part of their operations. The study also shows that of the of the four sectors in assessment, AI and AI-related technologies are being implemented the most in the earth observation sector. Given that the amount of data collected by satellites on a daily basis is of the magnitude of 150 terabytes– satellite operators are recognizing the need of using AI/AI related technologies to process and analyze the available data (Figure 15).

FIGURE 15: THE CURRENT USE OF AI TECHNOLOGIES IN EARTH OBSERVATION (TOTAL RESPONDENTS IN EARTH OBSERVATION SECTOR: 30)

In earth observation segment particularly, companies are dealing with unstructured and unlabeled data sets wherein no classification models exist. In this regard, the 22 respondents using ML/DL are of the opinion that algorithms bring significant analysis of the earth observation data to create an analysis motive and to make the data analytically sound. Simultaneously, as the earth observation industry witness’s multiple technology, and business model disruptions; the industry is percolating across industry verticals gather information about the earth’s natural resources and environment. Defense and intelligence, environment and forestry, agriculture, infrastructure and urban planning, retail and logistics and utilities are few of the critical industry sectors where earth observation industry is making use of AI and AI-based technologies. AI is emerging as a major technology driver enabling the earth observation industry to process and identify trends and patterns from huge volumes of satellite and UAV imagery (Figure 16).

22, 73%

8, 27%

YesNo

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FIGURE 16: KIND OF AI TECHNOLOGIES IMPLEMENTED IN EARTH OBSERVATION (TOTAL RESPONDENTS USING AI IN EARTH OBSERVATION SECTOR = 22)

A sectoral analysis highlights that ML/DL and image recognition/computer vision are the two most implemented AI technologies across all the industry user segments with high adoption in defense and intelligence, environment and forestry, agriculture, urban planning, and mapping (Figure 16). It can be comprehended from Figure 16, that the adoption of ML/DL and image recognition/computer vision software is found to be higher in agriculture and urban planning sectors. The use of AI on the huge volume of data collected – irrespective of resolution – helps derive strategic insights and valuable knowledge for the user stakeholder to make informed decisions and strategize. In defense and intelligence – while image recognition is found to be critical, computer vision enables object identification which is a critical information in war zones. The earth observation data collected by the military intelligence can be processed quickly for object and image classification along with land cover -helping the military to strategize their war game plan accordingly. Some of the algorithms in this segment involves high accuracy feature extraction – and thus requires a lot of fine tuning and training of algorithmic models which have to be undertaken to achieve high levels of accuracy. Further, in terms of agriculture, environment and forestry, and urban planning – a combination of AI technologies is being put to use i.e. image recognition and ML/DL. In these segments, image processing is of paramount importance and companies take the available satellite imagery, implement AI based algorithms on top of it and process it for change detection and predictive analysis. Particularly with respect to agriculture, AI usage has increased significantly over the last few years. The agriculture sector is one of the major users of satellite imagery and the implementation of AI along with GIS on it makes it possible to make evaluation of crop yield, and crop diseases, for instance. ML/DL algorithms are also powerful tool for processing and analyzing satellite imagery/or any imagery – of any resolution and provide nuanced insights. Today, since vast amounts of data is

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available from different types of sensors, at different spatial, temporal and spectral resolutions – AI helps in extracting valuable knowledge from complex data structures. From a utility and value perspective, AI crafted solutions enable seamless processing of information. ML/DL make it possible to process satellite imagery, detect and classify objects, capture accurate geographical features and monitor the minuscules of changes over a period of time. Case in point are the recent mergers and acquisitions by major earth observation analytic companies such as Ursa Space, Orbital Insight, Descartes Labs, Digital Globe, etc., to consistently expand the use of deep learning, neural networks and computer vision in their analytical workflows. Thus, application of ML/DL and computer vision is predominantly being used in object detection, land cover classification, change detection, target detection and data augmentation for the use of these approaches to show better predictive analytical trends, and performance than previous approaches of data processing and analysis. FIGURE 17: PURPOSE FOR WHICH AI IS IMPLEMENTED IN EARTH OBSERVATION (TOTAL RESPONDENTS USING AI IN EARTH OBSERVATION SECTOR = 22)

The study highlights that at present AI and AI-based technologies are mostly being used in the EO-downstream sector i.e. by companies which are into satellite data processing and analytics and the value-added services segment. In the EO-upstream (payloads and manufacturing and launch services and satellite and ground segments), the implementation of AI is still at an experimental stage and the PhiSat of the European Space Agency in this case is an exciting innovation. However, companies which are in the EO-downstream segment are traditionally EO data providers, or EO-data analytics/value added applications/services providers. Because the market is found to be shifting from asking just data – but value-added solutions, the downstream EO providers are now using AI to process and analyse the peta/tera bytes of data collected and provide integrated solutions to customers for effective decision making. Using AI/ML/DL and image recognition and computer vision, EO downstream companies are now able to provide ‘useful and analytical’ imagery and analytics to capture the entirety of the value chain and therefore, more of the market. ML/DL algorithms are being used intelligent analysis of the earth observation data and

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digital models are being created to provide effective value-added services to the final user. Traditional earth observation players are thus found to be collaborating with analytical AI-based start-ups to develop accurate and reliable solutions for the industry sectors and provide value-added services at large (Figure 17).

CONCLUSION The adoption of AI and AI-based technologies, particularly, ML/DL and image recognition/computer vision is found to be the highest in the earth observation industry (as compared to the four sectors under consideration for this study). The earth observation industry – which is primarily a geospatial industry – entails an industry in possession of huge volumes of data that needs to be processed and analyzed to derive critical insights. Understanding the relevance of AI, earth observation industry can be said to be one of the first industries to have adopted AI and AI based technologies to simplify its workflows. The value for the earth observation industry today is not to provide raw data but to provide insightful data to make faster data-driven decisions – and therefore the especially the EO downstream sector has begun using AI/ML/DL to develop algorithms to provide value-added solutions.

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LOCATION ANALYTICS AND BUSINESS INTELLIGENCE (LA/BI)

A confluence of location analytics and business intelligence is the perfect boost for any business to grow. In today’s world, mobile devices are omnipresent and huge amount of location data is being generated from these every day. Since this is the age of the fourth industrial revolution - the age of innovation - location technology is becoming the key differentiator to improve customer experiences, increase revenue generation and enhance operational efficiency. The power of location data lies in identifying the audiences, target-users to gain competitive insights about the market and observe offline consumer behavior. Often neglected, these are extremely important parameters which have a positive impact on businesses when monitored properly to generate high returns from the market. More recently, the advancements in AI and AI-based technology and its incorporation in business intelligence has enabled enterprises to enjoy what used to be once the realm of science fiction. The use of AI to derive location analytics and the business intelligence is foreseen to improve the marketing, management, supply chain, and logistics, among other things across a business enterprise. In location analytics and business intelligence context, AI and machine learning, can be put to use to build highly complex algorithms with unprecedented levels of accuracy which in turn will help in the development of strategic and intelligent solutions. Location-based enterprises are now able to use ML/DL and write algorithms to identify recent trends and insights from a vast realm of data and make faster decisions that potentially allow them to maintain a competitive advantage in real-time. Primarily, AI has been rapidly transforming the workflows of different industries. Irrespective of their sizes, enterprises are looking to leverage AI and location data to improve the efficiency of business processes and deliver smarter and more specialized and customized customer experiences. As seen in other sectors, AI powers the modern decision-making techniques. Prior to the advent of AI, executives had to depend on incomplete and inconsistent location data which in many cases did not yield analytical insights. However, today, AI feeds on big data, chews it and then breaks it down into actionable insights helping the executives in their decision making. For example, earlier, a marketing manager had to understand the ever-changing customer needs and align products and marketing strategies to meet those needs –but now, they can use AI to study the customer behavior in seconds. With the power of AI simulation and modeling of the location data, prediction of consumer behavior is becoming simpler. This enables businesses to cull out intelligence from the analyzed location data and make decisions that result in significant profit for the enterprises. AI technologies are able to draw intelligence from current and historical geographical data or location data to streamline their business processes by algorithms offering tailor made insights. Thus, the AI capabilities can be put to use in location-based businesses wherein applications are developed using demographic data – location, timing, etc. Almost all sectors, smart cities to autonomous vehicles, finance to healthcare –use location analytics and business intelligence – to make data-driven decisions and drive new business models and it is only a matter of time when

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AI implementation is not a choice but a necessity. Though AI and LA/BI have important fundamental differences, they together are the source of a powerful product which helps enterprises explore and invest in intelligence to its full potential and also helps businesses solve their greatest challenges and reach newer heights. FIGURE 18: CURRENT AND FUTURE USE OF AI IN LA/BI8

Location intelligence solutions are expanding across the diverse industry segments – ease of implementation and integration with AI solutions and prevalence of location data within business data. At present, most of the location intelligence solutions are implemented across the logistics and mobility, e-commerce, media and advertising, and BFSI. The future of location intelligence is not seen as a standalone line of technologies, but a solution embedded with larger IT and business intelligence systems. On one hand, location intelligence will enrich the context of the data that AI needs to function properly, AI will expand the ways in which location intelligence itself can enrich is functionality and reach new user bases. The Location Analytics and Business Intelligence Report 2019 by Geospatial Media and Communications highlights the current adoption of AI and AI based algorithms is higher in the e-commerce and the media and advertising sectors (Figure 18). 9 However, the study also highlights that in the future, the adoption of AI is going to shift significantly towards logistics and mobility, Banking and FinTech and hospitality and tourism as and when compared to the other sectors (Figure 18).

8 Location Analytics and Business Intelligence Report, 2019 by Geospatial Media and Communications

9 Location Analytics and Business Intelligence Report, 2019 by Geospatial Media and Communications

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FIGURE 19: CORRELATION BETWEEN INNOVATION INFRASTRUCTURE AND LIKELY IMPACT OF IMPACT OF AI IN DRIVING THE LI INDUSTRY

The extent of AI development within a country depends not only on the government focus, funds and supporting regulations, but also on the innovation infrastructure of the country. Geospatial Media’s analysis finds that there exists a strong correlation between the innovation infrastructure and the likely impact of AI in driving the LA/BI industry. The Location Analytics and Business Intelligence Report 2019 by Geospatial Media and Communications highlights that the countries with a well-developed innovation infrastructure are found to play a more crucial role in the utilization of AI in the LA/BI. It is found, USA, China, Germany, the Netherlands, UK and Canada are the forerunners with the requisite innovation infrastructure in place – thus – in these countries AI is expected to play a major role in the evolution of the location intelligence ecosystem (Figure 19). The responses that we have received through our online questionnaire and our interaction with the respective stakeholders who contribute or are using AI and AI based technology highlights that 46% of the 24 respondents of the LA/BI sector i.e. 11 respondents are currently using AI technologies while the remaining 13 respondents are at present not using based tools and technologies (Figure 20). Many location-based companies such as NVIDIA, Pitney Bowes, Quadrant, HERE Maps (data), TomTom are found to be actively developing and implementing AI-based technology to develop long-term business intelligence solutions. At present, the value of quick, yet accurate and real-time data is tremendous, and it is found that most enterprises – especially retail, healthcare and financial services are investing the use of AI and increasing their investments in AI solutions derive location-based business intelligence. The inferences so disseminated by the AI solutions are effective for formulating strategic business plans, decision-making and future business directions.

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FIGURE 20: THE CURRENT USE OF AI TECHNOLOGIES IN LOCATION ANALYTICS & BUSINESS INTELLIGENCE (TOTAL RESPONDENTS IN LA/BI = 24)

In terms of industry verticals, our 11 respondents who are using AI in LA/BI solutions are using AI based solutions along with location data across key industry sectors such as banking, retail, insurance, real estate, telecom and logistics and supply chain (Figure 21). From all the AI technologies in implementation – image recognition/computer vision and machine/deep learning technologies are found to be the preferred AI technologies. Image recognition software is used subsequently in retail, banking, and real estate while ML/DL is used mostly in logistics and supply chain, real estate and insurance. In the particular case of the retail industry, image recognition software is foreseen as a retail innovation and is found to be crucial for the retail business. In a report by Gartner, it is expected that the potential for image recognition – 85% customer interactions in the retail industry will be managed by AI. Further, computer vision is used to audit product placement of products in retail by digitizing store checks and gathering key information. Using deep neural networks, computer vision detects the products on the shelves – classifies them based on category, brand and item. Image recognition software is used to analyze photographs of shelves and give insights into consumer behavior, preferences and placements of products on display thus contributing to product placement – and securing maximum RoI from the placements and advertising messages. AI technologies are used in real-time, allowing the retail industry to optimize costs and boost sales. Further AI technologies can be used to identify and predict the future performance of a potential retail location (Figure 21).

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FIGURE 21: KIND OF AI TECHNOLOGIES IMPLEMENTED IN LOCATION ANALYTICS & BUSINESS INTELLIGENCE (TOTAL RESPONDENTS USING AI IN LA/BI = 11)

In the study, our respondents are also using to revolutionize logistics and supply chain. ML/DL are making it possible for companies to identify the patterns of the supply chain data using algorithms which pinpoint influential factors for a supply chain network’s success. These key factors could be the inventory levels, drawing production plans and transportation routes, forecasting demand and much more. Using ML/DL, companies are able to derive new knowledge and insights and predict the future demands for production, reduce freight costs, develop collaborative supply chain networks and minimize supplier risks. Of the total 11 respondents – nine respondents are using ML/DL to analyze data of the logistics and supply chain companies to generate high-end profiles of clients and the points of delivery. Using AI, companies are able to (re)produce the route optimizer used in the past and optimize their future route schedules. Thus, AI is used for visual pattern recognition to translate and transform the supply chain operations and management. Furthermore, software providers are combining other technologies with AI/ML/DL tools such as IoT sensors, cloud and big data and geospatial technologies to enable real-time monitoring providing visibility to supply chain platforms. Simultaneously, few of the respondents are using ML/DL for real-estate valuation. AI-based technologies are being used to optimize the process of buying and selling properties. While AI in real estate is currently slow, the technology is utilized in indirect ways. For instance, building automation systems wherein, sensors are placed in strategic areas of the building to collect data related to energy efficiencies, security systems, etc., among many other things. The huge volume of data collected – seasonally, weekly, daily – can be processed via ML/DL applications to predict a picture for the real-estate managers to understand what’s happening in their buildings/real estates under different circumstances (Figure 21).

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FIGURE 22: PURPOSE FOR WHICH AI IS IMPLEMENTED IN LOCATION ANALYTICS AND BUSINESS INTELLIGENCE (TOTAL RESPONDENTS USING AI IN LA/BI = 11)

The respondents who are using AI and AI-based technologies state that the main purpose of using AI in LA/BI is to derive insights from the data collected through various sensors. Companies in location analytics – retail, banking, real estate, logistics and supply chain, etc., require AI-based technologies to explore and analyze data. AI and AI-based algorithms make it easier for companies to process data to predict trends which enables effective decision-making. Further, the processed and analyzed data helps companies to find and build AI-backed solutions to meet their business goals and needs. In this regard, ML/DL is the preferred AI solution used in the LA/BI sector, followed by image recognition/computer vision technologies (Figure 22). Our analysis from the online survey and interaction with the respondents brings forth ML/DL is used for the purpose of defining business needs, exploring and analyzing data, predictive analysis/trends and finding the best solutions to make decisions. At present, sound recognition and processing are not being used across the location analytics and business intelligence segment. LA/BI segment is being powered by the combination of AI and the modern GIS in real time. GIS can correlate location data with time and space and integrate it with other critical information. GIS and location data together provides context to data collected by organizations, thus, leading to easy definition of business needs, and better prediction of trends. It is also found that the more data elements the GIS catalogs have – the more rewarding are the results generated by AI and AI-based technologies. Further, organizations are beginning to understand how GIS and location powered AI data helps customers towards data-driven decision making. Thus, location data along with GIS strengthens the AI-based decision-making process in the LA/BI sector. Of the 11 respondents using LA/BI, all 11 respondents state that they are using AI along with GIS and spatial analytics to enhance their deliverables of location analytics and business intelligence (Figure 23).

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Similarly, geo-referenced data generated from aerial LiDAR and earth observation satellites and positioning data generated from GNSS and positioning technology is empowered by AI and AI backed technologies to predict trends, draw business intelligence and help businesses make decisions. A case in point are the retail and logistics and supply chain companies who are able to use GPS, location and AI to optimize their routes and improve operational efficiency. Since both earth observation and GNSS and Positioning also provide businesses with geo-referenced data – AI is used with geospatial technologies for LA/BI. Since scanning technologies do not have any role to play in the LA/BI segment – AI is not being implemented with scanning technologies for this particular segment. CONCLUSION While the sample size is less, geospatial companies operating in the LA/BI segment are implementing AI technologies – especially, ML/DL and image recognition/computer vision across the varied industry segments such as retail, logistics and supply chain, real estate, among many other. Geospatial companies are using AI technologies for object classification, image recognition, visual pattern recognition, etc., to define business needs, analyze data, and conduct predictive analysis for informed decision making. In addition, AI is used with GIS and location data to draw business intelligence and derive critical actionable insights.

More companies in the LA/BI segment are looking to develop AI algorithms which are able to merge different data sources – i.e. geospatial data, location data, and other non-spatial data (business/economic data) to derive business intelligence. The data sources for all the above-mentioned types of data are available in silos and do not communicate with each other – it is AI technologies which make it possible for the different datasets to merge and communicate with each other. AI technologies such as machine/deep learning enables the different LA/BI datasets to communicate with each other and makes it possible for companies to extract valuable insights, and market intelligence from the available datasets.

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SMART CITIES Artificial intelligence has the potential to be an integral part of smart city projects and smart city applications. AI algorithms are crucial for automating cities and improving the operations of the local municipal activities to make the cities competitive and environmentally sustainable. Most of the geospatial companies involved in the development of smart cities believe - are of the opinion that to realize the aspirations of intelligent cities, AI algorithms and AI-based technology and applications are critical. While AI technologies have today moved from sci-fi fanfiction to digital reality, the adoption of AI in smart city projects has yet not evolved. While organizations are realizing the potential of AI to address real-life city challenges; the implementation of AI technology in smart city projects is still not substantial – and is at a nascent stage. Today, the implementation of AI smart city projects can be applicable in strategic components of smart city projects especially involving smart grids, Intelligent Transport Systems (ITS), solar rooftops, etc. Further, the government of many developing countries – especially the likes of India, China, and Indonesia have signaled AI to be a disruptive and a priority technology – and require the smart city projects implementing organizations to recognize the transformational impact AI can make in urban infrastructure development as well as aid cities to leapfrog the stages of smart city development. In terms of geospatial and AI in smart city projects, it is important to note that the smart cities of tomorrow are going to be built upon the huge volumes of spatial and non-spatial data that is available with the cities today. As cities generate petabytes of data through multitude of sensors, mobile phones, cameras, traffic management systems, smart meters, vehicles, etc., in function every second of the day; the role of AI comes into play to analyze and transform the data collected into actionable intelligence, to optimize the performance of the cities, engagement with the cities, and to enable conversant decision-making. The many examples on how AI can potentially be used in smart city projects are: • AI helps to solve the traffic situation, which is a bane for many cities. Real-time data being

collected via traffic management systems (TMS), vehicles, cars, and sensors is massive for human beings to manually assist traffic systems in the country. AI solutions can help comprehend real-time traffic and, in a flash, calculate permutations to process data at a local office to manage traffic and turn traffic lights on and off intelligently, thus solving the problem at the core.

• AI enhances public safety and security by enhancing surveillance technologies by easing the process of pattern monitoring, linking crime databases, etc.

• AI based technologies can assist in numerous city functions to help with crowd management, improve public accessibility, citizen service delivery, maintenance of outdoor spaces, parking management, etc.

AI and AI-based technologies are, thus, foreseen to address some of the most persistent challenges faced by the cities of today. Geospatial companies are wishful about bringing AI and AI-based applications and algorithms to urban applications to solve the many problems of the cities – however, the current adoption is significantly less.

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Our analysis from the online questionnaire highlights that of the total 92 survey responses – 20 stakeholders have responded on the use or the lack of use of AI in smart cities. Of these 20 responses, only five respondents i.e. 25% of the total respondents of the smart cities sector are currently found to be using AI and AI-based technologies in smart city projects while the remaining 15 respondents denied using AI and AI based technologies in smart cities (Figure 24). The five respondents who are currently using AI and AI based technologies in smart city projects have only recently begun to use AI technologies as part of the larger solution package in pilot projects. This means that the current bandwidth of implementation of AI tools is found to be at an experimental and embryonic stage. The survey numbers here are contrary to the otherwise over-enthusiastic approach towards AI and AI based urban applications. While, there are a couple of good examples of AI being used in smart city applications especially in Singapore, Amsterdam, and London, these applications are primarily being used to improve the traffic and parking situations in cities. FIGURE 24: THE CURRENT USE OF AI TECHNOLOGIES IN SMART CITY PROJECTS (TOTAL RESPONDENTS IN SMART CITY PROJECTS = 20)

Of the five different types of AI technologies that can be implemented/adapted in smart city projects are namely, Natural Language Processing (NLP), image recognition/computer vision systems, sound recognition and processing, ML/DL and robots. Our five stakeholders who are found to be using AI at present, recognize image recognition/computer vision technology as the backbone of all the components of the smart city projects – smart governance, smart mobility, smart environment, smart economy, smart living and smart society (Figure 26). Advances in AI has resulted in the creation of applications and algorithms that have image recognition capabilities – which basically means – object identification, human face identification, among many other things. In real-time, AI-based facial recognition software can resolve the problem of traffic in many countries. A case in point is New York – where the NYC - Department of Transportation is using image recognition and machine learning to evaluate the traffic jams, accidents, pedestrian’s behavior (jaywalking), parking violations, etc. The combination of image recognition and ML technology – along with the APIs which work together in harmony –can enhance e real-time information for analytical consumption and informed decision making. The use of AI on the massive amount of data collated and produced in real-time is aims to ensure traffic and parking management. For instance, in Netherlands, a geospatial company is able to create several

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derivative products using AI technologies on street imagery to create a highly panoramic view of the streets and improve the city-wide street planning. As the world is moving towards autonomous vehicles, AI based technologies can help in better traffic arrangement by including autonomous vehicles. Therefore, as it is clear from Figure 26 – across all smart city projects, image recognition/computer vision software is the leading AI technologies in smart city projects. All the five respondents who are using AI technologies in smart city projects prefer to use image recognition/computer vision technology in smart city projects. FIGURE 26: KIND OF AI TECHNOLOGIES IMPLEMENTED IN SMART CITY PROJECTS (TOTAL RESPONDENTS USING AI IN SMART CITY PROJECTS = 5)

Following the image recognition/computer vision software, the five respondents claim that sound recognition and processing is the second preferred adopted AI technology in smart city projects. While speech recognition is found to be critical, sound is found to be ‘vital’ for AI. Sound recognition is imperative to provide context to the surroundings and the environment. In this regard, some sounds are small and may not have a complex meaning – however, in other cases, sound recognition could potentially require immediate reactions. Sound recognition is also connected with IoT – an essential feature of intelligent connected devices – thereby enabling devices to intelligently understand context, presence, entertainments, security breach, activity, and many other things. In our analysis, we find the adoption of sound recognition and processing to be the second most recognized technology in smart city projects with the maximum adoption being in smart environment (Figure 26). While speaking of AI – NLP is just as important as the sound recognition application. NLP ensures effective communication between humans and machines using a natural language. A study by Gartner, titled, ‘2018 World AI Industry Development Bank’, estimated the global NLP market to

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be worth US$ 16 billion by 2021. 10 From this market share of global NLP – in future, a major proportion is expected to come from adoption of the NLP – AI technology in smart city projects. NLP can be effectively used to understand what citizens are saying and help them in the most sophisticated way possible. Additionally, as smart devices are the core component of the smart city project -NLP plays a critical role in understanding and reporting the human communication with the devices with ease. For smart mobility and a smart economy, NLP is the foundation. Our survey finds the industry recognizes the importance and need of implementing AI-NLP in smart city projects and predict that the segment will grow by leaps and bounds in the near future as and when the awareness of the technology grows (Figure 26). In our online survey, we also asked our stakeholders about the adoption of AI across the different phases of smart city development – i.e. plan, design, build and operate. The five respondents who are presently using AI in smart city projects use image recognition/computer vision actively across all the phases of smart city development. Further, in the planning stage of smart cities, ML/DL is utilized more as compared to the other AI-based technologies. It is found that to plan smart lighting, smart parking and to address other critical city problems, machine learning algorithms and neural networks are used to tackle the issues of urban development (Figure 27). FIGURE 27: IMPLEMENTATION OF AI TECHNOLOGY ACROSS THE DEVELOPMENT PHASE OF SMART CITIES (TOTAL RESPONDENTS USING AI IN SMART CITY PROJECTS = 5)

Data mining techniques along with ML/DL techniques, are critical in transforming the way data is captured, processed and used to plan smart city applications and provide best adaptive solutions for smart city planning. Using the data from sensors, databases, and intelligent applications and tools – machine learning helps in data-driven planning of smart cities. Further on, NLP and sound

10 https://www.huawei.com/en/about-huawei/publications/winwin-magazine/33/why-natural-language-processing-is-ais-jewel-in-the-crown

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recognition and processing is also used by few respondents across the four stages of smart cities development. Amongst all the geospatial technologies, it is found that at present, all respondents using AI technologies are using it alongside GIS (Figure 28). Also known as GeoAI, AI with GIS stresses on adding the spatial context to AI-algorithms. Additionally, specially from a smart city perspective, GeoAI can gain immense traction in health, heathcare related services and precision-related medicines. The primary reason why GeoAI will work is because there is availability of real-time feed of the environment via smart phone applications, cameras and videos. The huge volumes of data is collected, collated, , sorted, analyze and stored. In this process, AI technologies have a critical role to play as they can help derive insights and clear the ‘noise’ from the large sums of data ensuring accuracy and precision. The intersection of AI and GIS, thus, opens up opportunities that were not possible before. FIGURE 28: AI USE WITH GEOSPATIAL TECHNOLOGIES IN SMART CITY PROJECTS (TOTAL RESPONDENTS USING AI IN SMART CITY PROJECTS = 5)

Additionally, geo-tagged data collected from different sources such as scanning, earth observation and location data from GNSS and Positioning is being artificially transformed to cull out intelligence from the data. Respondents state that the data captured from the three geospatial technology sources is further processed in the GIS+AI tool to capture and model the environment, creating analysis from the multitudes of permutations and combinations to draw intelligence for smart city projects (Figure 28). This structural transformation of how data is processed and analyzed improves the many possibilities of the smart city projects – traffic congestion, public safety, logistics, surveying, infrastructure, healthcare, etc. CONCLUSION Smart cities are gaining popularity all across the world and there is an increasing realization among all stakeholders regarding the role AI and AI based technologies and algorithms in this segment. The smart cities segment is dependent on exponentially huge amount of data – big data which can be ‘intelligently’ transformed and modified to draw analysis and greatly contribute to the urban fabric and livability dimensions of a smart city. Image recognition/computer vision

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software, NLP, sound recognition, and ML/DL are thus foreseen to be central to the successful integration of culture, governance and mobility in urban planning and development. Policy makers and decision makers all across the globe are looking at the integration of AI and geospatial tools to intelligently model smart cities to a successful reality. Countries which are at the beginning phase of developing smart cities, may now look to implement AI and AI based technologies in their smart city projects to improve the decisional process – especially the strategic planning that is much required in the smart city’s projects. Cities can plan to use AI not in silos but with other emerging technologies such as big data, cloud and IoT sensors to ensure decision delivery support system for the successful delivery of smart city projects. AI along with geospatial technologies are at the core of the smart city ecosystem and it is up to the project owners to implement AI-based geospatial solutions to ensure higher productivity, and operational efficiency in their smart city projects.

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STUDY CONCLUSION AI and AI-based technologies (ML/DL, robotics, image recognition and computer vision, NLP) are not ‘buzz words’ anymore. This study finds that the confluence of geospatial and AI cannot be ignored and is the way of the future. Even though the current adoption of AI-based geospatial solutions is particularly at an experimental/embryonic stage – this is foreseen to increase over a period of time if the industry is able to address the few challenges that are hindering the development of the technology. In varied sectors – especially the areas for which this study has been undertaken - earth observation, location analytics and business intelligence, smart cities and construction and engineering - the level of AI adoption differs. The study finds, AI adoption to have gradually increased in the earth observation and the LA/BI sector – while being almost negligible in the construction and engineering and smart cities sector. It is also highlighted that the digital readiness of an industry – i.e. areas which have imbibed digital technologies at an early stage and can optimize their workflows – are also the fastest adopter of AI – for instance: the earth observation sector. On the contrary, the construction and engineering industry is found to be one of the least scoring industry sectors in digital readiness, is also a laggard in adopting AI in its construction workflows. Further, because most sectors today deal with large volumes of spatial and non-spatial data, the use of AI technologies – especially ML/DL and image recognition/computer vision is found extremely useful. These technologies and their algorithms are aimed at object identification, image classification, change detection, target detection, mapping of certain geographical features, and monitoring the minuscules of changes over a period of time. Additionally, the use of AI-based algorithms on the large sets of data collected by a multitude of sensors (IoT, geospatial technologies, etc.) helps companies to define their business needs, analyze data, predict trends and therefore, in the long run also enhance the decision-making process. Additionally, industry sectors today are beginning to recognize the value of GeoAI (Spatial sciences + AI), - i.e. combining innovations in the geospatial industry, and the different AI technologies – ML/DL, image recognition/computer vision to process and exploit the exponentially high amount of data for valuable knowledge. Also, geospatial technologies when used in coordination and combination with AI technologies, lead to strategic modelling, assessments and evaluations – to assist stakeholders in informed decision-making. AI algorithms are, thus developed to incorporate and accommodate spatial contexts and characteristics (in different resolutions and scales) with ease. Presently, GeoAI is being used in sectors wherein location data is of paramount importance – for instance, retail, real estate, logistics and supply chain and healthcare. In terms of future adoption, adoption of AI is set to rise in the next few years – as companies and users both realize that AI leads to improvement in productivity and efficiency, project time and cost savings and faster decision-making. However, the naysayers of AI technology, believe there still exists a technology gap in skills and knowledge, hindering AI adoption at a large scale. Geospatial companies do concur that they would like to understand the increasing possibilities of AI in different sectors (and also at an organizational level); however, it is not a risk stakeholder are willing to take any time soon.

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ANNEXURE I Annexure 1 is the online questionnaire from which 92 responses were generated: Questionnaire ABOUT YOUR ORGANIZATION Ques 1 - Your Contact Details

o Name: o Company/Organization: o Country of Residence: o Email Address: o Phone:

Ques 2 - Company Headquarters Ques 3 – Number of employees in your organization globally

o Self employed o 1-10 o 11-50 o 51-200 o 201-500 o 501-1000 o 1001-5000 o 5001-10000 o 10,000+

Ques 4 – When was your organization founded? Ques 5 - What is your company’s annual revenue?

o Less than $10 million o $10 million to < $100 million o $100 million to < $1 billion o $1 billion or more

Ques 6 – Under which of the following categories do you plan to participate in this survey? o Smart Cities o Construction and Engineering o Location Analytics and Business Intelligence o Earth Observation

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SMART CITIES Ques 7 – Is your organization currently using AI technologies for Smart City projects?

o Yes o No

Ques 8 – What kind of AI technologies are you currently implementing in Smart City projects?

Natural Language Processing

Image Recognition/ Computer Vision

Sound Recognition and Processing

Machine/ Deep Learning

Robotics N/A

Smart Governance

Smart Mobility Smart Environment

Smart Economy Smart Living

Smart Society If any other, please specify: Ques 9 – In which process of Smart Cities are you currently implementing AI technologies?

Natural Language Processing

Image Recognition/Computer Vision

Sound Recognition and Processing

Machine/ Deep Learning

Robotics N/A

Plan Design Build Operate

If any other, please specify: Ques 10 – Are you using AI for Smart Cities in combination with any of the below geospatial technologies? Yes No GNSS and Positioning Scanning Earth Observation GIS/Spatial Analytics

CONSTRUCTION AND ENGINEERING

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Ques 11 – Is your organization currently using AI technologies for Construction and Engineering projects?

o Yes o No

Ques 12 – What kind of AI technologies are you currently implementing in Construction and Engineering projects?

Natural Language Processing

Image Recognition/Computer Vision

Sound Recognition and Processing

Machine/ Deep Learning

Robotics N/A

Roads Tunnels Bridges Airports Railroads Facilities Buildings Dams Utilities

If any other, please specify: Ques 13 – In which process of Construction and Engineering projects are you currently implementing AI technologies?

Natural Language Processing

Image Recognition/ Computer Vision

Sound Recognition and Processing

Machine/ Deep Learning

Robotics N/A

Plan Design Build Operate

If any other, please specify: Ques 14 – Are you using AI for Construction and Engineering projects in combination with any of the below geospatial technologies?

Yes No

GNSS and Positioning Scanning Earth Observation GIS/Spatial Analytics

LOCATION ANALYTICS AND BUSINESS INTELLIGENCE

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Ques 15 – Is your organization currently using AI technologies for Location Analytics and Business Intelligence projects?

o Yes o No

Ques 16 – What kind of AI technologies are you currently implementing in Location Analytics and Business Intelligence projects?

Natural Language Processing

Image Recognition/ Computer Vision

Sound Recognition and Processing

Machine/ Deep Learning

Robotics N/A

Banking Retail Insurance Real Estate Telecom Logistics and Supply Chain

If any other, please specify: Ques 17 – In which process of Location Analytics and Business Intelligence projects are you currently implementing AI technologies? Natural

Language Processing

Image Recognition/ Computer Vision

Sound Recognition and Processing

Machine/ Deep Learning

Robotics N/A

Define business needs

Explore data Analyze data

Predict trends

Find the best solutions

Make decisions

If any other, please specify: Ques 18 – Are you using AI for Location Analytics and Business Intelligence projects in combination with any of the below geospatial technologies?

50

Yes No

GNSS and Positioning Scanning Earth Observation GIS/Spatial Analytics

EARTH OBSERVATION Ques 19 – Is your organization currently using AI technologies in earth observation?

o Yes o No

Ques 20 – What kind of AI technologies are you currently implementing across Earth Observation projects?

Natural Language Processing

Image Recognition/ Computer Vision

Sound Recognition and Processing

Machine/ Deep Learning

Robotics N/A

Defense & Intelligence

Environment and Forestry

Agriculture Trading Urban Planning Mapping Weather and Climate

If any other, please specify: Ques 21 – In which process of Earth Observation are you currently implementing AI technologies?

Natural Language Processing

Image Recognition/ Computer Vision

Sound Recognition and Processing

Machine/ Deep Learning

Robotics N/A

EO Upstream – Payloads Manufacturing and Launch Services

EO Upstream – Satellite and Ground segments

EO Downstream – Satellite Data

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Processing & Analytics EO Downstream – Value Added Services

If any other, please specify: Ques 22 – Are you using AI for Location Analytics and Business Intelligence projects in combination with any of the below geospatial technologies? Yes No

GNSS and Positioning Scanning Earth Observation GIS/Spatial Analytics

AI ADOPTION AT AN ORGANIZATION LEVEL Ques 23 – What is the Level of Maturity of AI projects in your organization?

o Investigating the use of AI o Implemented pilot projects in one department o Planning to expand to other departments o Using in multiple departments o Planning for enterprise-wide implementation o Employed enterprise-wide

Ques 24 – How long has your organization been using AI?

o Less than 1 years o 1 to 3 years o More than 3 years

Ques 25 – How important are the following technologies for your organization? o Natural Language Processing o Image Recognition/Computer Vision o Sound Recognition and Processing o Machine/Deep Learning o Robots

Ques 26 – From a success point of view, how important are each of the following factors in your AI projects?

o Productivity gains o Improved customer service/interface o Improvement in work quality o Final project cost reduction o Reduction of HR cost o Faster product development o Better sales analytics

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o Better production and operation analytics o Better data archiving and data mining o New market segment penetration/service

Ques 27 – Does your organization have any plans of using AI in the next two years? o Yes o No

Ques 28 – What is inspiring your organization to consider the implementation of AI technologies in the near future?

o Increase productivity and operational efficiencies o Cost savings by optimizing resource utilization o Save time by automating processes o Make faster business decisions o Avoid ‘human error’ o Improve customer experience o Others (please specify)

Ques 29 – If your organization is not currently using AI and not planning to use it either in the near future, what are the key reasons for it?

o Cost o Lack of trained HR o Lack of trust in the technology o Lack of clarity on government policies and laws o Ethical issues related to use of AI o Don’t see value in using AI at the present o Unavailability of reliable consultants and vendors to guide and/or implement the

technology o Others, please specify

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ANNEXURE II Interviewed stakeholders for qualitative assessment of the study: 1. Category: Earth Observation (all recorded)

1. Stuart Miller, Global Surface Intelligence, UK 2. Carolyn Savoldelli, World Resources Institute, UK 3. Jake, Candid, USA 4. Peter, Orbica, Germany 5. Taras, OPT/NET BV 6. Anne, Radiant Earth, USA 7. Agnieszka, Planet, USA 8. Kumar Navulur, Maxar, USA 9. Alexis Hannah Smith, ImGeospatial, UK (written interview) 10. Hamed Alemohammad, Chief Data Scientist, Radiant Earth Foundation, USA

2. Category: Construction and Engineering 1. Remon Pot, Fugro, The Netherlands (recorded) 2. Hessel, Kaios, The Netherlands (recorded) 3. Paulus Eckhardt, Director Design and Engineering, Ballast Nedam (written interview) 4. Kyle Tan, CEO, Co-founder, Airsquire AI, The Netherlands (written interview) 5. Dr. Pari, Head of Geospatial, L&T Next, India (written interview) 6. Jeff Muller, Director, Pix4D (written interview) 7. Wouter Seyen, Sitemark, Belgium (written interview)

3. Category: Smart Cities 1. Gideon Bleumink, Cyclomedia, The Netherlands 2. Marino, City of Bologna 3. Kshitij Batra, CEO, Teal India, India 4. Bart De Lathouwer, President, OGC 5. Irakli Beridze, Head, Centre for AI and Robotics, The Netherlands (written interview)

4. Category: Location Analytics and Business Intelligence (all recorded) 1. Xavier Ruiz, CEO, Smart Monkey, Spain 2. Siva, Oracle, USA 3. Ramana, axiomGT, USA 4. Stanimira Koleva, SVP, HERE Technologies, Singapore 5. Vijay Kumar, CTO, Esri India, India 6. Pierluigi Casale, Group Data Scientist, TomTom, The Netherlands 7. Anand and Joe, Pitney Bowes, USA 8. Harsh Govind, Microsoft