Kaubukowski, KennNsonga, BaldwinKretzschmar, VanessaAnnanias, YvesWiegreffe, DanielHlawitschka, MarioKlein, MaikeKrupka, DanielWinter, CorneliaWohlgemuth, Volker2023-11-292023-11-292023978-3-88579-731-9https://dl.gi.de/handle/20.500.12116/43100In the context of the renaturation of discontinued open-cast mines, the interactive visualization analysis of three-dimensional LiDAR provides a comprehensive overview for planning the subsequent use of these areas. When analyzing the measured point clouds, it is beneficial to classify the points, enabling the user to filter objects such as vehicles and buildings. Most current classification techniques for LiDAR data rely on good ground truth and work on specific types of measurements and resolutions. In this work, we present a semantic classification workflow for LiDAR data with highly heterogeneous acquisition and storage parameters. We apply the workflow to a freely available LiDAR data set and showcase the resulting classification. Our method is shown to reliably classify not only large-scale objects, such as buildings, but also small-scale objects, such as power lines and street lights.enGeographic Information SystemLiDARRenaturationText/Conference Paper10.18420/inf2023_1731617-5468