naco2018 county-wide 2d building footprints and 3d models ......to assure the quality of the...

Miami‐Dade County 1

County‐wide 2D building footprints and 3D models from LiDAR

Abstract of the Program Miami-Dade County has an increasing demand to make better decisions and communicate ideas more effectively. Our world is three-dimensional; there is a “z” factor, an elevation. It’s so important to understand events, projects, and operations in context. How do we model it? We need height.

Miami-Dade County’s GIS currently hosts over 1,200 point, line and polygon layers and 40 raster layers spanning 30 years of program development. In 2015 the County procured a county-wide LiDAR dataset. LiDAR (light detection and ranging) is a relatively new remote sensing technology that allows collecting very dense point samples of objects in 3D, storing x, y and “z” coordinates for each point.

The maker of our GIS software, ESRI, evolved tools transforming 2D GIS into 3D. Coupled with the LiDAR dataset, this provided a cost-effective means to create a county-wide building dataset and its corresponding 3D models.

The new dataset represents an enormous step forward in the representation of the County building infrastructure from points and large building polygons to a single county-wide dataset. It contains over 1 million polygon and multi-patch features of 2D footprints and 3D representations, providing a third dimension for county departments and citizens to perform a true-to-life spatial analysis.

The Problem/Need for the Program Since its inception 30 years ago, Miami-Dade County GIS has focused on providing a platform for complete and accurate data accessible to all county departments and the public. The GIS infrastructure has the goal of empowering the departments with the tools needed to provide a higher level of service and promote comprehensive and innovative solutions. Enriching the content of the enterprise geodatabase has been a key factor in accomplishing this effort.

The decision to create county-wide 2D and 3D building planimetric was triggered by the needs of county departments to map and spatially analyze problems such as: the impact of sea level rise to the community, the vertical impact of new and/or potentially new development to zoning codes, land use and transportation, the ability to perform line of sight calculations for cellular/radio towers, to add a 3D perspective to public safety scenarios, and the impact of buildings areas and volume in the storage capacity of each basin.

Planimetric data is a type of vector representation of geographic objects maintaining accurate horizontal measurements, examples of planimetric data are: water bodies, the edge of the pavement, street centerlines and building footprints.

The cost of traditional methods of planimetric feature extraction made the project infeasible. These methods base the data extraction process on tedious manual on-screen digitizing or stereo pair compilation techniques using orthophotography as the source of data. The process is labor intensive and extremely time-consuming.

With the rising growth of new technologies, an innovative methodology put together by ESRI elevated the use of classified LiDAR point cloud data to capture 2D and 3D building representations at a fraction of the cost. This new


approach combines existing tools with newly developed algorithms that perform a semi-automated feature extraction process ensuring high-quality results.

Description of the Program The project had the ambitious goal of extracting over one million buildings in 2D and 3D with some emphasis on adding a greater level of detail for iconic buildings in the Miami area. The time frame for the project was eight months. The project started in January of 2017 and ended by August of the same year.

Miami-Dade County performed project management, executed quality control processes, and engaged Esri consulting services to develop the tools and processes to successfully extract building footprints and ultimately 3D model Miami-Dade County.

2D Data Extraction

To facilitate the work, the county was divided into six areas. Each area was enumerated according to the expected delivery order.

Delivery Area Small Buildings Large Buildings Total Buildings Area (sq. miles) 1 562 440 1002 2.41 2 19236 4875 24111 26.51 3 90767 28906 119673 137.65 4 96607 24080 120687 70.68 5 97992 21819 119811 136.59 6 105053 15192 120245 246.72

Based on the delivery areas, LiDAR tiles were selected for each one of the areas. The label on each tile represents the SPE_ID, which was also used for identifying the orthophotography tiles that correspond to the area. Once all the tile files were identified for each area and placed into folders, these were used for creating LAS datasets.

The LAS dataset was checked for completeness of area coverage and reviewed for accuracy of point classification. It was loaded into a Mosaic Dataset. The Mosaic Dataset was created inside a delivery area file geodatabase. With a Mosaic dataset ready with the LAS classified for buildings, raster functions were applied to it to define on-the-fly processing operations to get better results on the shapes of the buildings.

The output of this process was exported to a .TIF file and converted to polygons. At this point the building footprints required to be regularized. Polygons were normalized by eliminating undesirable artifacts in their geometries.

During the extraction process, the buildings were classified based on their area as ‘small buildings’ when the area was greater than 160 sq. ft. and less than 5,000 sq. ft., and ‘large buildings’ when the area was greater than or equal to 5,000 sq. ft.

Additional criteria were defined for the 2D building footprint extraction:

1. >160 sq. ft. the minimum size for structures or holes 2. >= 5 ft. feature face 3. >= 5 ft. indent width and / or > 10 ft. length


4. >= 5 ft. arm width and / or >10 ft. length 5. No minimum spacing between features 6. Building footprints should not display a rotation greater than 5° from the building extent on the LiDAR. 7. Enforce edge squaring

8. No minimum vertex spacing

To assure the quality of the buildings footprint geometries the ANSI z1.4 sampling standards were followed. The accuracy of the data was defined as 98.5% meaning that in deliveries areas with the number of buildings between 35, 001 and 150, 000 no more than 14 errors would be considered acceptable. If 15 or more errors were found, the data will be considered unacceptable.

The acceptance criteria specifications included:

1. The collected building must contain the majority of significant character elements seen on source LiDAR or imagery where secondary information is needed. A simpler shape is preferred over a complex and ornate shape if the building is obscured by vegetation.

2. The collected building must contain at least 85% of a real extent as seen in comparison to LiDAR point features classified as ‘building’.

3. The collected building must be a permanent structure. 4. Awnings, balconies, bay windows, carports, covered walkways, overhangs, and staircases should not be

collected as part of the footprint. 5. Non-building structures such as HVAC equipment, signage, and screened enclosures should not be

collected as part of the building footprint. 6. Swimming pools on decks or in building stories above ground are not considered holes. 7. Automated QC cleanup routines like topology, minimum size, minimum hole size, the ratio of shape area

to perimeter will be used to detect geometry errors and flag them for later correction. 8. Buildings must be discrete, except common wall townhomes. 9. Coastlines, canals, ditches, levees, embankments, roads, paths, trails, railways, fences, walls, culverts, and

bridges may be used as seen on the imagery to aid in the cleanup but are not actual source features and do not participate in the building footprint collection methodology.

10. The source LiDAR data will be used as the primary data set to verify the accuracy and correctness of the extracted features from the automation process.

Automated data validation were implemented using Esri’s ArcGIS Desktop attribute and spatial queries as well as ArcGIS Data Reviewer extension checks and executed on one hundred percent of the extracted data. The following checks, in addition to any other checks deemed appropriate during the quality control of the data, were performed on the building footprints:

Evaluate Polygon Area Evaluate Polyline Length (Building faces) Cutbacks (square edges) Invalid Hole Feature Geometry on Geometry (Building over Building, Building over Parcel polylines) Vertex Count

Although automated validation was performed on the extracted data, visual data inspection was also necessary.


3D Model Generation

Esri’s Local Government Scene solution was used to create the 3D buildings. This is a set of ArcGIS Pro projects that can be used to author high-quality 3D buildings. These scenes are organized in different Levels of Detail (LOD) and derived from 2D building footprints. Once authored, the 3D scenes are a foundation for 3D workflows and applications; and provide a consistent geographic context across local government departments and agencies. The Schematic Local Government Scene project, which is a LOD2 (Level of Detail) 3D scene that includes the World Topographic Map draped over detailed elevation along with LOD2 buildings, was the selected solution. LOD2 building models have standard roof structures and were generated in a semi-automatic way. The 2D building needed additional segmentation to model complex roof structures. This solution provided methods to quality control the roof forms of the buildings. The Review Building Roof Forms step in the Publish LOD2 Buildings task provided useful information to perform a visual inspection of the data. The result from the Confidence Measurement tool was used to guide the review of the Roof-Form Extraction Process results. The Confidence Measurement tool reported the following measurement (attribute added to the building footprints):

RMSE: This field showed the root-mean-square-error of the generated building multipatch to the underlying surface model. The higher this number, the more likely that the roof-form extraction process encountered an error in classification.

Missed Planes: The MissedPlanes field flagged buildings where roof planes likely went undetected, based on the area of the planes detected compared to the area of the building footprint. This classification was broken into Low, Medium, High, and All categories. Low signifies a low probability of missed planes, while All signifies that there were no planes detected within the building.

Eave Height Underestimation: The LowEaveError field flagged buildings with eaves that are low compared to the total height of the building.

Although automated validation was performed on the extracted data, visual data inspection was also necessary. Visual review guidelines are to detect critical and minor errors and perform a corrective action. The entire set of 3D buildings was visually reviewed to ensure that no defects existed in the deliverable dataset. As in the previous process, the ANSI z1.4 sampling standards were followed to assure the quality of the building model geometries. The accuracy of the data was defined as 98.5%. The acceptance criteria specifications included:

1. (Child) 3D building acceptance criteria are predicated on successful (parent) 2D building footprint acceptance criteria.

2. All uncorrected 2D review defects will result in 3D defects. A hole in a 2D building will result in a hole in the 3D building.


3. A building composed of multiple roof planes, one which is flat and below the top-most roof plane(s) is considered a separate roof plane. It can be open to the outside on a side or (less often) it may be closed on all sides and only open to the sky. In such instances, a separate lower flat roof plane must be collected.

4. Parking garages may have inclined roof planes and auto ramps. Do not collect a sloped roof plane. Ignore the ramp and collect as flat.

5. The coordinate system of the 3D database must be consistent with the 2D output and the source LiDAR unless specifically mandated by customer’s Statement of Work (SOW).

6. 3D building shapes are not to be used for volumetric calculation or mass. 7. Total building height will be considered acceptable if the height of the largest roof plane ridgeline

matches the source LiDAR values within 98% of the building height 8. If a roof plane area is less than 25 sq. ft., it can then be depicted as part of an adjacent larger roof plane

component and does not require separate depiction. 9. If a roof plane elevation is not visually variant from larger adjacent roof planes when viewed at the agreed

upon scale (e.g. 1:500), it can then be depicted as part of the adjacent larger roof plane component and does not require separate depiction.

Automated data validation was implemented using designed ArcGIS Pro attribute and spatial query models on the created data. A tracking mechanism was implemented to monitor status and perform incremental project reviews.

Use of Technology The technology used for the project in various phases included:

Data extraction o ArcGIS ArcMap and 3D Analyst extension were used to extract 2D footprints o ArcGIS Pro and ESRI’s Local Government Scene template was used to create 3D models o 2015 LiDAR dataset o Small buildings point feature class o Large buildings polygon feature class

Data validation and quality control o ArcGIS Pro o 2015 LiDAR dataset o Small buildings point feature class o Large buildings polygon feature class o Pictometry Oblique Imagery

Data Distribution o ArcGIS Pro for web scene development and packaging o Miami-Dade County Enterprise Geodatabase o ArcGIS Online, https://mdc.maps.arcgis.com o Open Data site, https://gis-mdc.opendata.arcgis.com


The Cost of the Program The cost of the initial project was $363,000, which included:

Deliverable Description Type Fee

1 2D Building Footprint Extraction Quality Control Document Document

$80, 0002 3D Building Footprint Extraction Process Document Document

3 3D Building Creation Quality Control Document Document

4 2D Building Footprints Aggregated Delivery Data $135, 000

5 3D Building Models Aggregated Delivery Data $134 ,000

6 On-Site Knowledge Transfer Training $14, 000

Updates going forward are expected to average less than $35,000 a year.

The Results/Success of the Program Miami-Dade County using Esri consulting services to the successful implementation of the project. Miami-Dade County was the first county in the country to implement a county-wide 2D building footprint extraction and 3D building models based on LiDAR.

The project started January 2017 and was completed by August of the same year. As a result of the project, two new feature datasets were added to the enterprise geodatabase: 2D building footprints and 3D building models. This addition improved the level of GIS services to county departments and citizens.

Multiple county departments are using these new datasets to implement initiatives such as:

3D spatial analysis to identify the impact of sea level rise to the community. Vertical impact of new or potentially new development to zoning codes, land use, and transportation. Addition of a 3D perspective to public safety scenarios. Utilization of building footprint to express more accurately the impervious area not directly connected

to the drainage system. Use 3D-building’s volume to calculate the water path, as well as more accurate calculations of water

levels in flood modeling. Creation of the first county-wide 3D base map.


Worthiness of Award Miami-Dade County buildings planimetric dataset evolved from a point based feature class and only large buildings polygons to a full inventory of buildings with 2D footprints and 3D models.

Multiple Miami-Dade County departments are using the building planimetric dataset in unique ways:

The Regulatory and Economic Resources (RER) Department o Study of Resiliency and Vulnerabilities Assessment o Sea Level Rise Impact o Solar Capacity Assessment to install solar panels on county own properties

The Transportation Planning Organization o The Strategic Miami Area Rapid Transit Plan (SMART) is leveraging the use of 3D technologies

for a Land Use Evaluation Tools Office of Emergency Management

o Storm Surge Planning Information Technology Department

o County-wide 3D base map o The new 2D building footprint dataset and 3D models are published and freely downloadable

to the community via Miami-Dade County Open Data (https://gis-mdc.opendata.arcgis.com).

By embracing new technological advances and an innovative methodology Miami Dade County has been able to successfully complete this project with a total savings of over 2.5 million dollars. Prior to this solution various calculations had estimated its cost at over 3 million dollars making the project unattainable from a financial perspective.

With limited financial resources it is important that local and state governments stay open-minded and on the lookout for new opportunities. This project is an example of a successful story that has capitalized on available data and novel ideas; a project that has proven that innovation is key for a viable future and the only sustainable competitive advantage.


Supplemental Materials

Sample LiDAR

A collection of very dense point samples classified to include trees, buildings, roads, utility lines, and many other elements

Figure 1 LiDAR tile

Figure 2LiDAR points cloud


County Buildings Planimetric History

The original Miami-Dade County building planimetric dataset consisted of a point feature layer for small buildings (Figure 3) and a polygon feature layer for large buildings (Figure 4).

Figure 3 Miami-Dade County Small Buildings

Figure 4 Miami-Dade County Large Buildings


The new Miami-Dade County building planimetric contains a polygon feature layer for all buildings (Figure 5) and a multipatch feature layer for all building representations (Figure 6).

Figure 5 Miami-Dade County New 2D Building Planimetric

Figure 6 Miami-Dade County New Building 3D Models


Sea Level Rise

The Miami-Dade County Office of Resilience developed a study on sea level rise impact (Figure 7) for county own and leased properties.

Figure 7 Sea Level Rise Building Impacts


Transportation Planning

The Miami-Dade County Regulatory and Economics Resources department, in combination with the Transportation Planning Organization, developed a Land Use Analysis Tool (Figure 8) in support of the county’s SMART (Strategic Miami Area Rapid Transit Plan) Corridors initiative.

Figure 8 Land Use Analysis Tool and Capacity Indicators


Emergency Management

The Miami-Dade County Office of Emergency Management performed storm surge planning analysis (Figure 9) for emergency preparedness.

Figure 9 Storm Surge Planning

naco2018 county-wide 2d building footprints and 3d models ......to assure the quality of the...

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