Volume XLII-4/W18
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4/W18, 843–849, 2019
https://doi.org/10.5194/isprs-archives-XLII-4-W18-843-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4/W18, 843–849, 2019
https://doi.org/10.5194/isprs-archives-XLII-4-W18-843-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

  18 Oct 2019

18 Oct 2019

AERIAL POINT CLOUD CLASSIFICATION WITH DEEP LEARNING AND MACHINE LEARNING ALGORITHMS

E. Özdemir1,2, F. Remondino2, and A. Golkar1 E. Özdemir et al.
  • 1Skolkovo Institute of Technology (SkolTech), Moscow, Russia
  • 23D Optical Metrology (3DOM) unit, Bruno Kessler Foundation (FBK), Trento, Italy

Keywords: point cloud, classification, machine learning, deep learning, urban areas, geometric features

Abstract. With recent advances in technology, 3D point clouds are getting more and more frequently requested and used, not only for visualization needs but also e.g. by public administrations for urban planning and management. 3D point clouds are also a very frequent source for generating 3D city models which became recently more available for many applications, such as urban development plans, energy evaluation, navigation, visibility analysis and numerous other GIS studies. While the main data sources remained the same (namely aerial photogrammetry and LiDAR), the way these city models are generated have been evolving towards automation with different approaches. As most of these approaches are based on point clouds with proper semantic classes, our aim is to classify aerial point clouds into meaningful semantic classes, e.g. ground level objects (GLO, including roads and pavements), vegetation, buildings’ facades and buildings’ roofs. In this study we tested and evaluated various machine learning algorithms for classification, including three deep learning algorithms and one machine learning algorithm. In the experiments, several hand-crafted geometric features depending on the dataset are used and, unconventionally, these geometric features are used also for deep learning.