The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Download
Publications Copernicus
Download
Citation
Articles | Volume XLII-4/W15
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4/W15, 111–115, 2019
https://doi.org/10.5194/isprs-archives-XLII-4-W15-111-2019
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4/W15, 111–115, 2019
https://doi.org/10.5194/isprs-archives-XLII-4-W15-111-2019

  23 Sep 2019

23 Sep 2019

APPLICABILITY OF NEURAL NETWORKS FOR IMAGE CLASSIFICATION ON OBJECT DETECTION IN MOBILE MAPPING 3D POINT CLOUDS

J. Wolf, R. Richter, S. Discher, and J. Döllner J. Wolf et al.
  • Hasso Plattner Institute, Faculty of Digital Engineering, University of Potsdam, Germany

Keywords: Mobile Mapping 3D Point Cloud, Semantic Classification, Image Segmentation, Artificial Neural Network, Deep Learning, Object Detection

Abstract. In this work, we present an approach that uses an established image recognition convolutional neural network for the semantic classification of two-dimensional objects found in mobile mapping 3D point cloud scans of road environments, namely manhole covers and road markings. We show that the approach is capable of classifying these objects and that it can efficiently be applied on large datasets. Top-down view images from the point cloud are rendered and classified by a U-Net implementation. The results are integrated into the point cloud by setting an additional semantic attribute. Shape files can be computed from the classified points.