Center of Excellence for Geospatial Information Science (CEGIS)
CEGIS researchers are Federal scientists in the USGS that lead collaborative research projects.
CEGIS researchers are Federal scientists in the USGS that lead collaborative research projects.
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).
Research
Research
Each subject is a science theme with associated subtopics. You'll find research information, projects, images, and publications available by topic and by general search.
Publications
Grammar to graph—An approach for semantic transformation of annotations to triples Grammar to graph—An approach for semantic transformation of annotations to triples
Data annotation is the process of labeling data to show the outcome that a related data model should predict. In this study, annotation data were transformed into semantic graph triples, mainly for use with the Resource Description Framework (RDF), a type of entity-relationship-attribute data model for graph databases. The transformation of annotation data to semantic graph triples...
Transfer learning with convolutional neural networks for hydrological streamline delineation Transfer learning with convolutional neural networks for hydrological streamline delineation
Hydrological streamline delineation is critical for effective environmental management, influencing agriculture sustainability, river dynamics, watershed planning, and more. This study develops a novel approach to combining transfer learning with convolutional neural networks that capitalize on image-based pre-trained models to improve the accuracy and transferability of streamline...
GeoAI for science and the science of GeoAI GeoAI for science and the science of GeoAI
This paper reviews trends in GeoAI research and discusses cutting-edge ad- vances in GeoAI and its roles in accelerating environmental and social sciences. It ad- dresses ongoing attempts to improve the predictability of GeoAI models and recent re- search aimed at increasing model explainability and reproducibility to ensure trustworthy geospatial findings. The paper also provides...
Science
2025 CEGIS Annual Research Meeting
The Center of Excellence for Geospatial Information Science (CEGIS) was proud to host the 2025 CEGIS Annual Research Meeting July 22-24, 2025. The annual research meeting is a special occasion to celebrate our accomplishments, explore ongoing projects, and foster collaboration for future endeavors. This year's gathering featured a broad range of presentations from our researchers and their...
Image processing
Image processing helps us study and understand different types of landscapes. We use algorithms and software to analyze satellite and airborne images to make detailed maps of the land and its features. By using automated techniques such as feature extraction and building of 3D models, image processing helps scientists learn more about the land and how it changes over time.
Hydrography/hypsography integration
Hydrography/Hypsography integration, aka Hydro/hypso, involves combining information about water (hydro) and elevation (hypso) to study how water moves across different landscapes. It helps scientists understand where water flows, accumulates, and how it shapes the land. This helps scientists figure out how to manage water resources and predict flooding or erosion.
