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Center of Excellence for Geospatial Information Science (CEGIS)

The CEGIS vision is to conduct, lead, and influence the research and innovative solutions required by the National Spatial Data Infrastructure (NSDI) and the emerging GeoSpatial and GeoSemantic Web. CEGIS is a virtual organization with Federal and academic affiliate scientists conducting research in support of The National Map and the Three-Dimensional Elevation Program (3DEP).

 

The U.S. Geological Survey Center of Excellence for Geospatial Information Science (CEGIS) was created in 2006 and since that time has provided research primarily in support of The National Map. The presentations and publications of the CEGIS researchers document the research accomplishments that include advances in electronic topographic map design, generalization, data integration, map projections, sea level rise modeling, geospatial semantics, ontology, user-centered design, volunteer geographic information, and parallel and grid computing for geospatial data from The National Map. A research plan spanning 2013–18 has been developed extending the accomplishments of the CEGIS researchers and documenting new research areas that are anticipated to support The National Map of the future. In addition to extending the 2006–12 research areas, the CEGIS research plan for 2013–18 includes new research areas in data models, geospatial semantics, high-performance computing, volunteered geographic information, crowdsourcing, social media, data integration, and multiscale representations to support the Three-Dimensional Elevation Program (3DEP) and The National Map of the future of the U.S. Geological Survey.

Publications

A guide to creating an effective big data management framework

Many agencies and organizations, such as the U.S. Geological Survey, handle massive geospatial datasets and their auxiliary data and are thus faced with challenges in storing data and ingesting it, transferring it between internal programs, and egressing it to external entities. As a result, these agencies and organizations may inadvertently devote unnecessary time and money to convey data without
Authors
Samantha Arundel, Kevin G McKeehan, Bryan B Campbell, Andrew N. Bulen, Philip T. Thiem

Historical maps inform landform cognition in machine learning

No abstract available.
Authors
Samantha Arundel, Sinha Gaurav, Wenwen Li, David P. Martin, Kevin G McKeehan, Philip T. Thiem

Geomorphometric analysis of the Summit and Ridge classes of the Geographic Names Information System

This research aims to conduct a geosemantic comparison of landforms classified in the Summit and Ridge feature classes in the Geographic Names Information System (GNIS). The comparison is based on a 2D shape analysis of manually delineated polygons produced by USGS staff to correspond to 33,304 Summit and 8,006 Ridge features. Five shape measures were chosen for this specific geomorphometry-based
Authors
Sinha Gaurav, Samantha Arundel, Romim Somadder, David P. Martin, Kevin G McKeehan