The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLIII-B4-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B4-2020, 89–96, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B4-2020-89-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B4-2020, 89–96, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B4-2020-89-2020

  24 Aug 2020

24 Aug 2020

BUILDING FEATURES RECOGNITION FROM UNCERTAIN 3D LIDAR POINT CLOUDS: A SEMANTIC APPROACH

X.-F. Xing1,2 and M. A. Mostafavi1,2 X.-F. Xing and M. A. Mostafavi
  • 1Department of Geomatics, Laval University, Quebec, Canada
  • 2Centre for Research in Geospatial Data and Intelligence, Laval University, Quebec, Canada

Keywords: feature recognition, uncertainty, knowledge, semantic rules, LiDAR point cloud

Abstract. LiDAR technology allows rapid observation of high-resolution and precise 3D point clouds for diverse applications in urban and natural areas. However, uneven density and incomplete point clouds make LiDAR data processing more challenging for the extraction of semantic information on objects and their components. In this paper, we propose a knowledge based semantic reasoning solution for the recognition of building components (e.g. roofs) from segmentation results in the presence of uncertainties in LiDAR point clouds. The proposed solution uses a semantic reasoning approach as well as a similarity evaluation process for object recognition. We apply the proposed method to recognize buildings’ roof styles from a point cloud with uncertainty as a case study.