@inproceedings{eb75da590a7b49b3beb0c9911145d815,
title = "Automatic modelling of 3D trees using aerial LIDAR point cloud data and deep learning",
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.",
keywords = "ITC-GOLD",
author = "R.G. Kippers and L. Moth and {Oude Elberink}, S.J.",
note = "Publisher Copyright: {\textcopyright} 2021 International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives. All rights reserved.",
year = "2021",
month = jun,
day = "28",
doi = "10.5194/isprs-archives-XLIII-B2-2021-179-2021",
language = "English",
volume = "XLIII-B2-2021",
series = "International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences",
publisher = "International Society for Photogrammetry and Remote Sensing (ISPRS)",
pages = "179--184",
booktitle = "The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences",
edition = "B2-2021",
}