The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLIII-B3-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2020, 527–532, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-527-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2020, 527–532, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-527-2020

  21 Aug 2020

21 Aug 2020

EFFICIENT LARGE-SCALE AIRBORNE LIDAR DATA CLASSIFICATION VIA FULLY CONVOLUTIONAL NETWORK

E. Maset1, B. Padova2, and A. Fusiello1 E. Maset et al.
  • 1DPIA, University of Udine, Via delle Scienze, 206 – Udine, Italy
  • 2Helica s.r.l., Via fratelli Solari, 10 – Amaro, Italy

Keywords: LiDAR, Classification, Large-scale dataset, Deep Learning, Fully Convolutional Network

Abstract. Nowadays, we are witnessing an increasing availability of large-scale airborne LiDAR (Light Detection and Ranging) data, that greatly improve our knowledge of urban areas and natural environment. In order to extract useful information from these massive point clouds, appropriate data processing is required, including point cloud classification. In this paper we present a deep learning method to efficiently perform the classification of large-scale LiDAR data, ensuring a good trade-off between speed and accuracy. The algorithm employs the projection of the point cloud into a two-dimensional image, where every pixel stores height, intensity, and echo information of the point falling in the pixel. The image is then segmented by a Fully Convolutional Network (FCN), assigning a label to each pixel and, consequently, to the corresponding point. In particular, the proposed approach is applied to process a dataset of 7700 km2 that covers the entire Friuli Venezia Giulia region (Italy), allowing to distinguish among five classes (ground, vegetation, roof, overground and power line), with an overall accuracy of 92.9%.