Spatial AI and Data Science

The Center for Geographic Analysis (CGA) has extensive experience applying data science and AI methods to spatial problems, including work in geospatial modeling, neural network–based map analysis, spatial accessibility, remote sensing, and large-scale data infrastructure. 

CGA researchers and collaborators have developed methods for content-based raster map retrieval, network-based delineation of urban regions, and spatial Monte Carlo simulations, as well as containerized geospatial processing systems and open science platforms for spatiotemporal data sharing. 

The Center also supports applied studies using spatial accessibility models, explainable AI in geographic analysis, and integration of geospatial methods into cloud computing and visual programming environments.  See some of our relevant publications below: