Abstract
Most Congestion analysis are based on the urban traffic video surveillance, which depend on the quality of existing surveillance equipments. In this paper, we propose a novel method to perform congestion analysis by utilizing remote sensing images for undeveloped areas or disaster-affected areas where lack of traffic video surveillance. Firstly, the vehicles and extract road area is detected from remote sensing images using objects detection technique. Then the number of Vehicles in the road are counted and mapped into data instances. Finally, density-based clustering algorithm is adopted to find the locations which are probably the Congestion points. The experimental results on real world datasets demonstrate that the proposed method can perform congestion analysis effectively.
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Acknowledgements
This work was supported by National Key Research and Development Plan of China (2016YFC0803000, 2016YFB0502604), International Scientific and Technological Cooperation and Academic Exchange Program of Beijing Institute of Technology(GZ2016085103), and Frontier and Interdisciplinary Innovation Program of Beijing Institute of Technology(2016CX11006), National Natural Science Fund of China (61472039).
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Yuan, H., Yang, J., Li, X., Ma, S. (2018). Congestion Analysis Based on Remote Sensing Images. In: Yuan, H., Geng, J., Liu, C., Bian, F., Surapunt, T. (eds) Geo-Spatial Knowledge and Intelligence. GSKI 2017. Communications in Computer and Information Science, vol 848. Springer, Singapore. https://doi.org/10.1007/978-981-13-0893-2_37
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DOI: https://doi.org/10.1007/978-981-13-0893-2_37
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