ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Volume VI-4/W1-2020
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., VI-4/W1-2020, 13–20, 2020
https://doi.org/10.5194/isprs-annals-VI-4-W1-2020-13-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., VI-4/W1-2020, 13–20, 2020
https://doi.org/10.5194/isprs-annals-VI-4-W1-2020-13-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  03 Sep 2020

03 Sep 2020

AUTOMATIC DETECTION AND CHARACTERIZATION OF GROUND OCCLUSIONS IN URBAN POINT CLOUDS FROM MOBILE LASER SCANNING DATA

J. Balado1,2, E. González1, E. Verbree2, L. Díaz-Vilariño1,2, and H. Lorenzo1 J. Balado et al.
  • 1Universidade de Vigo, CINTECX, Applied Geotechnologies Research Group, Campus universitario de Vigo, As Lagoas, Marcosende 36310 Vigo, Spain
  • 2Delft University of Technology, Faculty of Architecture and the Built Environment, GIS Technology Section, 2628 BL Delft, The Netherlands

Keywords: urban environment, point clouds, occlusion detection, image processing, raster, object classification

Abstract. Occlusions accompany serious problems that reduce the applicability of numerous algorithms. The aim of this work is to detect and characterize urban ground gaps based on occluding object. The point clouds for input have been acquired with Mobile Laser Scanning and have been previously segmented into ground, buildings and objects, which have been classified. The method generates various raster images according to segmented point cloud elements, and detects gaps within the ground based on their connectivity and the application of the hit-or-miss transform. The method has been tested in four real case studies in the cities of Vigo and Paris, and an accuracy of 99.6% has been obtained in occlusion detection and labelling. Cars caused 80.6% of the occlusions. Each car occluded an average ground area of 11.9 m2. The proposed method facilitates knowing the percentage of occluded ground, and if this would be reduced in successive multi-temporal acquisitions based on mobility characteristics of each object class.