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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

Automated deep learning-based point cloud classification on USGS 3DEP lidar data using transformer

The goal of the U.S. Geological Survey’s (USGS) 3D Elevation Program (3DEP) is to facilitate the acquisition of nationwide lidar data. Although data meet USGS lidar specifications, some point cloud tiles include noisy and incorrectly classified points. The enhanced accuracy of classified point clouds can improve support for many downstream applications such as hydrologic analysis, urban planning,
Authors
Jung-Kuan (Ernie) Liu, Rongjun Qin, Shuang Song

Evaluation of classified ground points from National Agriculture Imagery program photogrammetrically derived point clouds

Studies have shown that digital surface models and point clouds generated by the United States Department of Agriculture’s National Agriculture Imagery Program (NAIP) can measure basic forest parameters such as canopy height. However, all measured forest parameters from these studies are evaluated using the differences between NAIP digital surface models (DSMs) and available lidar digital terrain
Authors
Jung-Kuan (Ernie) Liu, Samantha Arundel, Ethan J. Shavers

Assessing the utility of uncrewed aerial system photogrammetrically derived point clouds for land cover classification in the Alaska North Slope

Uncrewed aerial systems (UASs) have been used to collect “pseudo field plot” data in the form of large-scale stereo imagery to supplement and bolster direct field observations to monitor areas in Alaska. These data supplement field data that is difficult to collect in such a vast landscape with a relatively short field season. Dense photogrammetrically derived point clouds are created and are faci
Authors
Jung-Kuan (Ernie) Liu, Rongjun Qin, Samantha Arundel

Multimedia

Overhead image of blue water features with a hilly terrain in the background.
3D Hydrography Program Product Specification
3D Hydrography Program Product Specification
lidar-derived digital surface elevation models of a stream  channel
Lidar-derived digital surface and elevation models of a stream channel
Lidar-derived digital surface and elevation models of a stream channel
Lidar-derived digital elevation model and NHD stream lines
Lidar-derived digital elevation model and NHD stream lines
Lidar-derived digital elevation model and NHD stream lines
100m resolution elevation model with hydro
100m resolution elevation model with hydro
100m resolution elevation model with hydro
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