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 and Dennis Motsko
Evaluating conflation methods using uncertainty modeling
", Proc. SPIE 8747, Geospatial InfoFusion III, 874703 (May 23, 2013); doi:10.1117/12.2015321; http://dx.doi.org/10.1117/12.2015321