The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLIII-B2-2021
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2021, 247–254, 2021
https://doi.org/10.5194/isprs-archives-XLIII-B2-2021-247-2021
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2021, 247–254, 2021
https://doi.org/10.5194/isprs-archives-XLIII-B2-2021-247-2021

  28 Jun 2021

28 Jun 2021

EXPLORING CROSS-CITY SEMANTIC SEGMENTATION OF ALS POINT CLOUDS

Y. Xie1,2, K. Schindler3, J. Tian1, and X. X. Zhu1,2 Y. Xie et al.
  • 1Remote Sensing Technology Institute (IMF), German Aerospace Center (DLR), Oberpfaffenhofen, Germany
  • 2Data Science in Earth Observation, Technical University of Munich (TUM), Munich, Germany
  • 3Photogrammetry and Remote Sensing, ETH Zürich, Switzerland

Keywords: Point Clouds, Semantic Segmentation, Deep Learning, Transfer Learning, Domain Adaptation

Abstract. Deep learning models achieve excellent semantic segmentation results for airborne laser scanning (ALS) point clouds, if sufficient training data are provided. Increasing amounts of annotated data are becoming publicly available thanks to contributors from all over the world. However, models trained on a specific dataset typically exhibit poor performance on other datasets. I.e., there are significant domain shifts, as data captured in different environments or by distinct sensors have different distributions. In this work, we study this domain shift and potential strategies to mitigate it, using two popular ALS datasets: the ISPRS Vaihingen benchmark from Germany and the LASDU benchmark from China. We compare different training strategies for cross-city ALS point cloud semantic segmentation. In our experiments, we analyse three factors that may lead to domain shift and affect the learning: point cloud density, LiDAR intensity, and the role of data augmentation. Moreover, we evaluate a well-known standard method of domain adaptation, deep CORAL (Sun and Saenko, 2016). In our experiments, adapting the point cloud density and appropriate data augmentation both help to reduce the domain gap and improve segmentation accuracy. On the contrary, intensity features can bring an improvement within a dataset, but deteriorate the generalisation across datasets. Deep CORAL does not further improve the accuracy over the simple adaptation of density and data augmentation, although it can mitigate the impact of improperly chosen point density, intensity features, and further dataset biases like lack of diversity.