Cooperative Agreement for affiliated Partner with the Chesapeake Watershed Cooperative Ecosystem Studies Unit
Contract Overview
Solicitation details, issuing organization, response deadlines, documents, and interested companies for this government contract opportunity.
AI Contract Overview
The U.S. Geological Survey through the Department of the Interior is seeking a Cooperative Ecosystem Studies Unit partner under solicitation G26AS00149 to advance geospatial artificial intelligence applications for automated landform mapping, enhance the Landform Reference Ontology using knowledge graphs, and improve the positional accuracy of summit features in the Geographic Names Information System using lidar-derived elevation data. This cooperative agreement focuses on cutting-edge geospatial research aimed at modernizing federal landform classification and naming standards through innovative AI and data integration techniques, with performance expected to contribute directly to national geospatial infrastructure. The solicitation, posted on June 24, 2026, has a response deadline of July 24, 2026, and is open to affiliated partners of the Chesapeake Watershed Cooperative Ecosystem Studies Unit. Interested parties must coordinate through the designated point of contact, Rachel Miller, reachable via email at rachel_miller@ios.doi.gov or phone at 916-278-9331. The project is funded by the federal government and does not specify a set-aside type or NAICS code, indicating broad eligibility under the CESU framework. While the place of performance and office address details are not provided, all work is expected to support USGS missions within the broader context of the Department of the Interior’s geospatial initiatives.
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Full Description
The U.S. Geological Survey (USGS) is offering a funding opportunity to a Cooperative Ecosystem Studies Unit (CESU) partner for research focused on advancing GeoAI-enabled automated landform mapping, expansion of the Landform Reference Ontology using knowledge graphs, and improving Geographic Names Information System (GNIS) summit feature positional accuracy using lidar-derived elevation datasets.
