23 May 2013 Evaluating conflation methods using uncertainty modeling
Author Affiliations +
Proceedings Volume 8747, Geospatial InfoFusion III; 874703 (2013); doi: 10.1117/12.2015321
Event: SPIE Defense, Security, and Sensing, 2013, Baltimore, Maryland, United States
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.
© (2013) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Peter Doucette, John Dolloff, Roberto Canavosio-Zuzelski, Michael Lenihan, Dennis Motsko, "Evaluating conflation methods using uncertainty modeling", Proc. SPIE 8747, Geospatial InfoFusion III, 874703 (23 May 2013); doi: 10.1117/12.2015321; https://doi.org/10.1117/12.2015321
PROCEEDINGS
14 PAGES


SHARE
KEYWORDS
Computer simulations

Data modeling

Geographic information systems

Matrices

Correlation function

Error analysis

Monte Carlo methods

Back to Top