Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B2, 257-262, 2016
https://doi.org/10.5194/isprs-archives-XLI-B2-257-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
 
07 Jun 2016
CROWDSOURCING BIG TRACE DATA FILTERING: A PARTITION-AND-FILTER MODEL
X. Yang and L. Tang State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Wuhan 430079, China
Keywords: Crowdsourcing traces, Trace partitioning, GPS trace filtering, GPS baseline Abstract. GPS traces collected via crowdsourcing way are low-cost and informative and being as a kind of new big data source for urban geographic information extraction. However, the precision of crowdsourcing traces in urban area is very low because of low-end GPS data devices and urban canyons with tall buildings, thus making it difficult to mine high-precision geographic information such as lane-level road information. In this paper, we propose an efficient partition-and-filter model to filter trajectories, which includes trajectory partitioning and trajectory filtering. For the partition part, the partition with position and angle constrain algorithm is used to partition a trajectory into a set of sub-trajectories based on distance and angle constrains. Then, the trajectory filtering with expected accuracy method is used to filter the sub-trajectories according to the similarity between GPS tracking points and GPS baselines constructed by random sample consensus algorithm. Experimental results demonstrate that the proposed partition-and-filtering model can effectively filter the high quality GPS data from various crowdsourcing trace data sets with the expected accuracy.
Conference paper (PDF, 1182 KB)


Citation: Yang, X. and Tang, L.: CROWDSOURCING BIG TRACE DATA FILTERING: A PARTITION-AND-FILTER MODEL, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B2, 257-262, https://doi.org/10.5194/isprs-archives-XLI-B2-257-2016, 2016.

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