Varlik, AbdullahUray, Firat2024-02-232024-02-2320230039-62651752-2706https://doi.org/10.1080/00396265.2021.1996798https://hdl.handle.net/20.500.12452/12984The classification of LIDAR point clouds has always been a challenging task. Classification refers to label each point in different categories, such as ground, vegetation or building. The success of deep learning techniques in image processing tasks have encouraged researchers to use deep neural networks for classification of LIDAR point clouds. In this paper, we proposed a U-Net based architecture capable of classifying LIDAR data. The results indicated that our network model achieved an average F1 score of 91% over all three classes (ground, vegetation and building) for our best model.eninfo:eu-repo/semantics/closedAccessLidarDeep LearningPoint CloudsPoint Cloud ClassificationPoint Cloud SegmentationRemote SensingArticle5538821312-s2.0-85119301168Q2WOS:000717878100001Q410.1080/00396265.2021.1996798