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Proceedings Paper

Evaluating conflation methods using uncertainty modeling
Author(s): Peter Doucette; John Dolloff; Roberto Canavosio-Zuzelski; Michael Lenihan; Dennis Motsko
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Paper Abstract

The classic problem of computer-assisted conflation involves the matching of individual features (e.g., point, polyline, or polygon vectors) as stored in a geographic information system (GIS), between two different sets (layers) of features. The classical goal of conflation is the transfer of feature metadata (attributes) from one layer to another. The age of free public and open source geospatial feature data has significantly increased the opportunity to conflate such data to create enhanced products. There are currently several spatial conflation tools in the marketplace with varying degrees of automation. An ability to evaluate conflation tool performance quantitatively is of operational value, although manual truthing of matched features is laborious and costly. In this paper, we present a novel methodology that uses spatial uncertainty modeling to simulate realistic feature layers to streamline evaluation of feature matching performance for conflation methods. Performance results are compiled for DCGIS street centerline features.

Paper Details

Date Published: 23 May 2013
PDF: 14 pages
Proc. SPIE 8747, Geospatial InfoFusion III, 874703 (23 May 2013); doi: 10.1117/12.2015321
Show Author Affiliations
Peter Doucette, National Geospatial-Intelligence Agency (United States)
John Dolloff, National Geospatial-Intelligence Agency (United States)
Roberto Canavosio-Zuzelski, National Geospatial-Intelligence Agency (United States)
Michael Lenihan, National Geospatial-Intelligence Agency (United States)
Dennis Motsko, National Geospatial-Intelligence Agency (United States)

Published in SPIE Proceedings Vol. 8747:
Geospatial InfoFusion III
Matthew F. Pellechia; Richard J. Sorensen; Kannappan Palaniappan, Editor(s)

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