Machine Learning for Spatio-temporal Datasetsand Remote Sensing

Remote Sensing for Climate Modeling

Physics-based feature detectors combinedvia machine learning into compound classifier

Machine learning Physics-based

Terabyte Size Dataset per Simulation

Change Detection

Objective: Identify Changes (damaging changes – due to fires, insects, inclement weather conditions; changes in cropping patterns – e.g, “Soy” in one year to “Corn” in the other year)Data: MODIS NDVI time seriesExisting Studies: Varun Chandola, Ranga Raju Vatsavai: A scalable Gaussian process analysis algorithm for biomass monitoring. Statistical Analysis and Data Mining 4(4): 430-445 (2011)

Vegetation Classification

Objective: Land Use/Land Cover Classification. Also known as thematic classification is still challenging task. Challenges include overlapping signatures, temporal (phonological) dependencies, soil types and elevation changes, climatic regions, and more over the subjective definition of a “class.”Data: Multi-spectral and multi-temporal Landsat-8 imageryExisting Studies: Varun Chandola, Ranga Raju Vatsavai: Multi-temporal remote sensing image classification - A multi-view approach. CIDU 2010: 258-270Ranga Raju Vatsavai, Shashi Shekhar, Budhendra L. Bhaduri: A Semi-supervised Learning Algorithm for Recognizing Sub-classes. ICDM Workshops 2008: 458-467

Urban Neighborhood Classification

Objective: Identify different types of urban neighborhoods in very high-resolution satellite imagery. Often “pixel” and “object” based methods are not sufficient to identify to accurately identify different neighborhoods (e.g., formal vs. informal) in satellite imagery and requires novel approaches to characterize higher-order spatial patterns.Data: Very high resolution (VHR) areal imageryExisting Studies:Object-based: Ranga Raju Vatsavai: Object based image classification: state of the art and computational challenges. BigSpatial@SIGSPATIAL 2013: 73-80Multiple-Instance: Ranga Raju Vatsavai: Gaussian multiple instance learning approach for mapping the slums of the world using very high resolution imagery. KDD 2013: 1419-1426

Mobility and Physical Activity, Interactive User Interfaces

Actigraph GT3X

Modeling Mobility Behavior

User Internet Information:1.Spatial and location-based information (buildings)2.Temporal information (Sessions times and duration)3.Interest-based information (web domains visited)4.Load and traffic information (flow rate and packet rate)

Terabytes per week

Hierarchical Clustering

Change Detection

Publically Available Datasets

• http://www.quora.com/Where-can-I-find-large-datasets-open-to-the-public

• http://www.inside-r.org/howto/finding-data-internet