The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLIII-B2-2021
https://doi.org/10.5194/isprs-archives-XLIII-B2-2021-179-2021
https://doi.org/10.5194/isprs-archives-XLIII-B2-2021-179-2021
28 Jun 2021
 | 28 Jun 2021

AUTOMATIC MODELLING OF 3D TREES USING AERIAL LIDAR POINT CLOUD DATA AND DEEP LEARNING

R. G. Kippers, L. Moth, and S. J. Oude Elberink

Keywords: AHN, Aerial Laser Scanning, Point Cloud, PointNet, Deep Learning, CityJSON

Abstract. 3D tree objects can be used in various applications, like estimation of physiological equivalent temperature (PET). During this project, a method is designed to extract 3D tree objects from a country-wide point cloud. To apply this method on large scale, the algorithm needs to be efficient. Extraction of trees is done in two steps: point-wise classification using the PointNet deep learning network, and Watershed segmentation to split points into individual trees. After that, 3D tree models are made. The method is evaluated on 3 areas, a park, city center and housing block in the city of Deventer, the Netherlands. This resulted into an average accuracy of 92% and a F1-score of 0.96.