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Center of Excellence for Geospatial Information Science (CEGIS)

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CEGIS Research Plan 2013-2018

Presentations and Publications from CEGIS

Completed Projects


Current CEGIS Projects

Data Integration

Geospatial Semantics and Ontology

High-performance Computing (HPC)

Integration of NGP Data within Fluid Dynamic Applications

Multiscale Representation

National Terrain Mapping

Volunteered Geographic Information (VGI)

Academic Affiliates


The National Map

Home ← Multiscale Representation

Multiscale Representation

This project aims to define relations between map scale and geomorphological characteristics of cartographic features at the national level. Relations developed for multiscale cartographic features and multi-resolution derived features will help automate cartographic generalization and define ontology design patterns for topographic feature extraction.

Cartographic Data Integration

Features represented on a map or cartographic product must be contextually integrated in a logical fashion, which involves both the geometric (spatial) and attributed characteristics of represented features. This research aims to define and use multi-scale relations among cartographic features, including geomorphological characteristics, to refine contextual cartographic feature representations and associated generalizations.


Geospatial data are accessed, displayed, and used at a variety of resolutions and scales in cartographic and database forms by the USGS user community. The ultimate goal for this research is to provide a system to automatically generalize data from the most detailed version (i.e., 1:2,400 for the NHD in some areas) to the least detailed scale needed for mapping or modeling (1:17,00,000 for The National Map viewer), with the generalization process tailored to the need.

Topographic Feature Extraction (or Pattern Recognition)

CEGIS researchers have developed a high level topographic ontology for the vector thematic layers of The National Map. The ontology provides semantic definitions for retrieving and viewing, via the Web, particular instances of the basic topographic features that are available in these databases. This research aims to apply knowledge derived from cartographic data integration and generalization research to define ontology design patterns for extraction of complex topographic features.

More Information

Abstracts, Presentations, Publications

Project Team

Lawrence Stanislawski, Project Lead, CEGIS, NGTOC

Ellen Finelli, NGTOC

Ariel Doumbouya, NGTOC

Dr. Barbara Buttenfield, Department of Geography, University of Colorado-Boulder

Chris Anderson-Tarver, Department of Geography, University of Colorado-Boulder

Dr. Cynthia Brewer, Department of Geography, Pennsylvania State University

Paulo Raposo, Department of Geography, Pennsylvania State University

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Last modified: Monday, 30-Jan-2017 17:46:53 EST