The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLIII-B2-2022
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2022, 351–358, 2022
https://doi.org/10.5194/isprs-archives-XLIII-B2-2022-351-2022
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2022, 351–358, 2022
https://doi.org/10.5194/isprs-archives-XLIII-B2-2022-351-2022
 
30 May 2022
30 May 2022

VECTORIZATION OF URBAN MLS POINT CLOUDS: A SEQUENTIAL APPROACH USING CROSS SECTIONS

E. Barçon1, T. Landes2, P. Grussenmeyer2, and G. Berson1 E. Barçon et al.
  • 1Innovation department, TT Géomètres-Experts, 75011 Paris, France
  • 2Université de Strasbourg, CNRS, INSA Strasbourg, ICube Laboratory UMR 7357, Photogrammetry and Geomatics Group, 67000, France

Keywords: Mobile Laser Scanning, Vectorization, Point Clouds, Automation, Road features

Abstract. Dense point clouds acquired with a mobile laser scanning system (MLS) device become usual raw data for different surveyor applications: topographic maps, 3D models, road inventories, risk assessment of vegetation on road or railroads. Thanks to important evolutions in technologies, MLS devices became powerful and very popular. In the meantime, the need for point cloud automatic processing tools is growing. However, the available tools have not yet reached a sufficient level of maturity. Using MLS point clouds to produce topographic maps, BIM model or other deliverables, requires very often manual vectorization (or digitalization) work. In the road context, the transition from point cloud to road map that consists in delineating curb or road edges, road markings, pole, trees, facades etc. is currently performed manually. To reduce these time-consuming operations, several solutions have been proposed in the literature. In this paper we present the first results of a method consisting in vectorizing urban point cloud scene. The originality of this work is to propose a global approach aiming to detect and vectorize simultaneously multiple objects. The developed algorithm uses cross-section analysis to detect road curbs and vertical objects. The first results are promising, since an F-score higher than 80% has been reached, even before applying road logic rules or additional knowledge. The detection and extraction of vertical objects including facades, trees, and poles, is more challenging but the detections also present a recall greater than 85%.