Urban accessibility diagnosis from mobile laser scanning data |
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| Institution: | 1. Wuhan University, School of Resource & Environmental Science, 129 Luoyu Road, Wuhan, Hubei, PR China;2. Delft University of Technology, Faculty of Architecture and the Built Environment, OTB Research, GIS technology, Julianalaan 134, 2628 BL Delft, The Netherlands;1. Department of Geography, Ghent University, Krijgslaan 281 S8, Ghent 9000, Belgium;2. Cosmopolis Centre for Urban Research, Vrije Universiteit Brussel, Pleinlaan 2, 1050 Elsene, Brussels, Belgium;3. Bartlett School of Planning, University College London, 14 Upper Woburn Place, WC1H 0NN London, United Kingdom;4. Department of Geography, University of Tartu, Vanemuise 46, 51014 Tartu, Estonia;5. College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, 29 Yudao Street, Nanjing 210016, China;1. School of Geography and Earth Sciences, McMaster University, Canada;2. Department of Civil, Geological and Mining Engineering, École Polytechnique de Montréal, Canada;1. State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, CAS, Beijing 100101, China;2. University of Chinese Academy of Sciences, Beijing 100049, China;3. Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China;4. China Mobile Communications Group Beijing Co., Ltd., China |
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| Abstract: | In this paper we present an approach for automatic analysis of urban accessibility using 3D point clouds. Our approach is based on range images and it consists in two main steps: urban objects segmentation and curbs detection. Both of them are required for accessibility diagnosis and itinerary planning.Our method automatically segments facades and urban objects using two hypotheses: facades are the highest vertical structures in the scene and objects are bumps on the ground on the range image. The segmentation result is used to build an urban obstacle map. After that, the gradient is computed on the ground range image. Curb candidates are selected using height and geodesic features. Then, nearby curbs are reconnected using Bézier curves. Finally, accessibility is defined based on geometrical features and accessibility standards.Our methodology is tested on two MLS databases from Paris (France) and Enschede (The Netherlands). Our experiments show that our method has good detection rates, is fast and presents few false alarms. Our method outperforms other works reported in the literature on the same databases. |
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| Keywords: | Accessibility Soft-mobility Mathematical morphology Curbs Urban modeling Mobile laser scanning |
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