3D Point Cloud from Nakadake Sanroku Kiln Site Center, Japan: Sample Data for the Application of Adaptive Filtering with the AFwizard

This data set represents 3D point clouds acquired with LiDAR technology and related files from a subregion of 150*436 sqm in the ancient Nakadake Sanroku Kiln Site Center in South Japan. It is a densely vegetated mountainous region with varied topography and vegetation. The data set contains the original point cloud (reduced from a density of 5477 points per square meter to 100 points per square meter), a segmentation of the area based on characteristics in vegetation and topography, and filter pipelines for segments with different characteristics, and other data necessary. The data serve to test the AFwizard software which can create a DTM from the point cloud with varying filter and filter parameter selections based on varying segment characteristics (https://github.com/ssciwr/afwizard). The AFwizard adds flexibility to ground point filtering of 3D point clouds, which is a crucial step in a variety of applications of LiDAR technology. Digital Terrain Models (DTM) derived from filtered 3D point clouds serve various purposes and therefore, rather than creating one representation of the terrain that is supposed to be "true", a variety of models can be derived from the same point cloud according to the intended usage of the DTM. The sample data were acquired during an archaeological research project in a mountainous and densely forested region in South Japan -- the Nakadake-Sanroku Kiln Site Center: LiDAR data were acquired in a subregion of 0.5 sqkm, a relatively small area characterized by frequent and sudden changes in topography and vegetation. The point cloud is very dense due to the technology chosen (UAV multicopter GLYPHON DYNAMICS GD-X8-SP; LiDAR scanner RIEGL VUX-1 UAV). Usage of the data is restricted to the citation of the article mentioned below. Version 2.01: 2023-05-11; Article citation updated; 2022-07-21; Documentation (HowTo - Minimal Workflow) updated, data files tagged.