Volume XLII-2/W13
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W13, 103-110, 2019
https://doi.org/10.5194/isprs-archives-XLII-2-W13-103-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W13, 103-110, 2019
https://doi.org/10.5194/isprs-archives-XLII-2-W13-103-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

  04 Jun 2019

04 Jun 2019

CLASSIFICATION OF AERIAL POINT CLOUDS WITH DEEP LEARNING

E. Özdemir1,2 and F. Remondino2 E. Özdemir and F. Remondino
  • 1Space Center, Skolkovo Institute of Technology (SKOLTECH), Moscow, Russia
  • 23D Optical Metrology (3DOM) unit, Bruno Kessler Foundation (FBK), Trento, Italy

Keywords: point clouds, segmentation, classification, 3D building reconstruction

Abstract. Due to their usefulness in various implementations, such as energy evaluation, visibility analysis, emergency response, 3D cadastre, urban planning, change detection, navigation, etc., 3D city models have gained importance over the last decades. Point clouds are one of the primary data sources for the generation of realistic city models. Beside model-driven approaches, 3D building models can be directly produced from classified aerial point clouds. This paper presents an ongoing research for 3D building reconstruction based on the classification of aerial point clouds without given ancillary data (e.g. footprints, etc.). The work includes a deep learning approach based on specific geometric features extracted from the point cloud. The methodology was tested on the ISPRS 3D Semantic Labeling Contest (Vaihingen and Toronto point clouds) showing promising results, although partly affected by the low density and lack of points on the building facades for the available clouds.