ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume V-2-2020
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-2-2020, 703–710, 2020
https://doi.org/10.5194/isprs-annals-V-2-2020-703-2020
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-2-2020, 703–710, 2020
https://doi.org/10.5194/isprs-annals-V-2-2020-703-2020

  03 Aug 2020

03 Aug 2020

CHANGE DETECTION AND DEFORMATION ANALYSIS BASED ON MOBILE LASER SCANNING DATA OF URBAN AREAS

J. Gehrung1,2, M. Hebel1, M. Arens1, and U. Stilla2 J. Gehrung et al.
  • 1Fraunhofer IOSB, Ettlingen, Fraunhofer Institute of Optronics, System Technologies and Image Exploitation, 76275 Ettlingen, Germany
  • 2Photogrammetry and Remote Sensing, Technische Universitaet Muenchen, 80333 Muenchen, Germany

Keywords: Mobile Laser Scanning, Change Detection, Deformation Analysis, Occupancy Grid

Abstract. Change detection is an important tool for processing multiple epochs of mobile LiDAR data in an efficient manner, since it allows to cope with an otherwise time-consuming operation by focusing on regions of interest. State-of-the-art approaches usually either do not handle the case of incomplete observations or are computationally expensive. We present a novel method based on a combination of point clouds and voxels that is able to handle said case, thereby being computationally less expensive than comparable approaches. Furthermore, our method is able to identify special classes of changes such as partially moved, fully moved and deformed objects in addition to the appeared and disappeared objects recognized by conventional approaches. The performance of our method is evaluated using the publicly available TUM City Campus datasets, showing an overall accuracy of 88 %.