Abstract
Roads are, probably the most important features appearing in cartography, both digital and analog one. The necessary tasks, to get accurate roads representation, were traditionally really expensive: photogrammetry and in situ differential GPS, for example. Nevertheless nowadays, the web allows people to register waypoints in their navigation device, with low accuracy and offer them to the rest of community. This way a lot of traces could be available to infer a mean road axis which, probably to be much more precise than the individual ones. In this paper we present three approaches in order to compute the representative axis above mentioned: a) Fréchet distance concept, b) B-spline least square fit and c) genetic algorithm spline-based. This paper shows that all our approaches are suitable to be deployed in a web-based application in order to support collaborative digital cartography. The dataset we used in our study is composed of 149 traces captured by a low accuracy user consumer GPS.
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Ariza-López, F.J., Barrera, D., Reinoso, J.F., Romero-Zaliz, R. (2015). Inferring Mean Road Axis from Big Data: Sorted Points Cloud Belonging to Traces. In: Le Thi, H., Pham Dinh, T., Nguyen, N. (eds) Modelling, Computation and Optimization in Information Systems and Management Sciences. Advances in Intelligent Systems and Computing, vol 359. Springer, Cham. https://doi.org/10.1007/978-3-319-18161-5_38
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DOI: https://doi.org/10.1007/978-3-319-18161-5_38
Publisher Name: Springer, Cham
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