Geospatial Data Modeling

We design automated tools that validate and enhance the spatial accuracy of critical material data by integrating and conflating open-source geospatial information. Our capabilities cross-reference geolocated addresses and special interest dataset inventories with publicly available data—including Points of Interest (POIs) from widely used platforms—to assess and refine location intelligence. This process supports international security and nonproliferation efforts by enabling precise verifications at the site or facility level.

We have the capability to leverage global, open-source data to understand and model city-level activity patterns—even in regions where authoritative data is limited or unavailable. By harnessing spatially explicit Points of Interest (POIs) and other open datasets (like buildings, road networks, and neighborhoods), we can deliver fine-resolution insights into how urban areas function across space and time.

By mapping any and all available data with rich attributes—such as business type, hours of operation, and user activity signals—we can capture the pulse of a city. This enables dynamic analysis of land use distributions, mobility patterns, and temporal activity zones, supporting critical applications like emergency response, urban planning, and high-resolution mapping of residential and non-residential spaces.

Geospatial Data Modeling team members conduct research on geometric primitives and vector data engineering, spatial data fusion, large scale spatial data workflows, as well algorithm and software development for geospatial applications. Educational backgrounds span geospatial science, environmental informatics, remote sensing and data science. Additionally, researchers are proficient in geospatial and remote sensing techniques, application of machine learning on large spatial datasets, image classification, data engineering , data conflation, and geospatial intelligence.