Integration of variable filter parameters for one filtering process on 3D point cloud data
A project at the SSC/IWR at Heidelberg University, officially named “Human-in-the-Loop Adaptive Terrain Filtering of 3D Point Clouds for Archaeological Prospection : Open Source Python Library for Adaptive Terrain Filtering of Laser Scanning Data”.
Together with colleagues from Heidelberg University and the Universität Wien, a Python library is being developed for human-in-the-loop adaptation of parameters for ground filtering of 3D point clouds acquired with LiDAR.
In order to obtain task related digital terrain models from LiDAR point clouds, a ground-point filtering step with a set of parameters related to environmental conditions (e.g. vegetation, topography) is essential. Up to date, filter parameters have to be found and set for one run over the whole region and represent an overall compromise. Our library shall adapt parameters of a filter according to user feedback and automatically to changing environments in one run and communicate with leading filter software. Thus, archaeological prospection will become significantly streamlined and dramatically increase in quality.
Project finished with release v1.0.0b9.
DONEUS, Michael, Bernhard HÖFLE, Dominic KEMPF, Gwydion DASKALAKIS & Maria SHINOTO. 2022. Human-in-the-loop development of spatially adaptive ground point filtering pipelines—An archaeological case study. Archaeological Prospection 29: 503–24. https://doi.org/10.1002/arp.1873.
SHINOTO, Maria, Michael DONEUS, Hideyuki HAIJIMA, Hannah WEISER, Vivien ZAHS, Dominic KEMPF, Gwydion DASKALAKIS, Bernhard HÖFLE & Naoko NAKAMURA. 2022. 3D point cloud from Nakadake Sanroku Kiln Site Center, Japan: sample data for the application of adaptive filtering with the AFwizard. https://doi.org/10.11588/data/TJNQZG.