GeoAI and Deep Learning Symposium: Spatial Machine Learning and GeoAI Case Studies

Type: Paper
Sponsor Groups: Cyberinfrastructure Specialty Group, Geographic Information Science and Systems Specialty Group, Spatial Analysis and Modeling Specialty Group, Esri
Poster #:
Day: 4/5/2019
Start / End Time: 9:55 AM / 11:35 AM (Eastern Standard Time)
Room: Washington 6, Marriott, Exhibition Level
Organizers: Orhun Aydin
Chairs: Orhun Aydin

Call for Submissions

The session will contain showcases from ESRI. Researchers from industry and academia are also encouraged to submit an abstract to showcase applications of their work on spatial machine learning. Of particular interest are:

- Deep learning networks and their applications for modeling spatio-temporal prediction and classification
- Works pertaining to comparing traditional GIS analysis to machine learning methods


Intersection of machine learning and GIS is ever increasing for modeling and predicting complex spatial and spatio-temporal phenomena. Niche GIS applications ranging from ecological modeling to prescriptive sales volume prediction have been benefiting from the developments in generic machine learning methods, including deep learning. In addition, the number of spatial machine learning methods that model space and time implicitly have been on the rise. In this session, applied papers that integrate machine learning methods to spatial problems will be presented. In addition, use cases that juxtapose spatial machine learning methods to their non-spatial counterparts will be demonstrated. Session is intended to demonstrate areas where machine learning has been opening new avenues of solutions and addressing the limitations of traditional analysis methods in GIS.


Type Details Minutes Start Time
Presenter Amin Tayyebi*, ESRI, Daniel Wilson, ESRI, Omar Maher, ESRI, Shairoz Sohail, ESRI, High Resolution Land Cover Mapping across the Conterminous USA using Deep Learning 20 9:55 AM
Presenter Shairoz Sohail*, Esri, Omar Maher, Esri - Manager, Amin Tayyebi, Esri - Collegue, Daniel Wilson, Esri - Collegue, Rohit Singh, Esri - Collegue , Automated Detection and Classification of Damaged Structures using Deep Learning 20 10:15 AM
Presenter Francesco Serafin*, Colorado State University, Olaf David, Colorado State University, Charles Ehlschlaeger, CERL US Army Engineer Research and Development Center, Andre Dozier, Colorado State University, Jack Carlson, Colorado State University, FeNS: Framework-enabled NEAT-based Surrogate modeling 20 10:35 AM
Presenter Roberto Ponce-Lopez*, Instituto Tecnologico de Estudios Superiores de Monterrey, Using Google Place API and machine learning to characterize non-work destinations 20 10:55 AM
Presenter Dong Luo*, , Marcellus M. Caldas, Kansas State University, Paulo De Marco Junior, Federal University of Goias, Estimate how pollination service affect soybean yield in the Brazilian Cerrado using remote sensing and multidisciplinary approaches 20 11:15 AM

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