26 October 2016 Identification and correction of road courses by merging successive segments and using improved attributes
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Abstract
Both in military and civil applications, there is an urgent need for a highly up-to-date road data, which should be ideally semantically structured (into main roads, walking paths, escape ways, etc.) with application-driven attributes, such as road width, road type, surface condition and many others. A vectorization algorithm processing aerial images recently acquired yields an up-to-date road vector data, which are, however, often represented by wriggly, noisy polylines without semantics. The reasons for zigzagged street courses are insufficiencies in the intermediate results of sensor data processing (orthophotos, elevation maps) and occlusions caused by trees, buildings, and others. In the current contribution, an improved computation of geometric attributes will be explained which makes a difference between straight and circular (or elliptic) polylines. Using improved attributes, the candidates for polylines having identical course and sharing a junction are determined. From such candidates, we form chains of polylines. These chains correspond better to the intuitive perception of the term street than the previously used road polylines, because, even after being interrupted by narrower side roads, a chain maintains its label. The generalization of chains with simultaneously adjusting positions of junctions is evidently performed. We apply a generalization with the purpose-based modification of a well-known polyline simplification algorithm once chain-wise and once polyline-wise in order to show - by means of qualitative results - the advantages of the chain-wise generalization.
Conference Presentation
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Dimitri Bulatov, Dimitri Bulatov, Gisela Häufel, Gisela Häufel, Melanie Pohl, Melanie Pohl, } "Identification and correction of road courses by merging successive segments and using improved attributes", Proc. SPIE 10008, Remote Sensing Technologies and Applications in Urban Environments, 100080V (26 October 2016); doi: 10.1117/12.2239208; https://doi.org/10.1117/12.2239208
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