LiDAR data have become indispensable for research in archaeology and a variety of other topographic applications. To derive products (e.g. digital terrain or feature models, individual trees, buildings), the 3D LiDAR points representing the desired objects of interest within the acquired and georeferenced point cloud need to be identified. This process is known as classification, where each individual point is assigned to an object class. In archaeological prospection, classification focuses on identifying the object class ‘ground points’. These are used to interpolate digital terrain models exposing the microtopography of a terrain to be able to identify and map archaeological and palaeoenvironmental features. Setting up such classification workflows can be time-consuming and prone to information loss, especially in geographically heterogeneous landscapes. In such landscapes, one classification setting can lead to qualitatively very different results, depending on varying terrain parameters such as steepness or vegetation density. The paper focusses on a special workflow for optimal classification results in these heterogeneous environments, which integrates expert knowledge. It presents a novel Pythonbased open-source software solution, which helps to optimize this process and creates a single digital terrain model by an adaptive classification based on spatial segments. The advantage of this approach for archaeology is to produce coherent digital terrain models even in geomorphologically heterogenous areas or areas with patchy vegetation. The software is also useful to study the effects of different algorithm and parameter combinations on digital terrain modelling with a focus on a practical and time-saving implementation. As the developed pipelines and all meta-information are made available with the resulting data set, classification is white boxed and consequently scientifically comprehensible and repeatable. Together with the software’s ability to simplify classification workflows significantly, it will be of interest for many applications also beyond the examples shown from archaeology.
Ground point filtered DTM of a 1200 x 900m² large area at the foot of Mount Nakadake. The area is characterized by steep slopes and very dense vegetation. The created DTM performing well in the central area, where most of the kiln sites are located but was less successful in the border areas. 1) Sample area used in the paper containing kiln sites, 2) Paddy terraces, 3) Border areas.
Find all details in the full paper:
Doneus, M., Höfle, B., Kempf, D., Daskalakis, G., & Shinoto, M. (2022). Human-in-the-loop development of spatially adaptive ground point filtering pipelines — An archaeological case study. Archaeological Prospection, 1–22.
Code and data availability:
The software project of AFwizard is open-source and publicly available on GitHub. The data used in the case study are available online under a creative common licence here. We are convinced that it will help archaeological prospection to become significantly streamlined and dramatically increase in quality. Not at least, the software will be of great use to anyone who has to deal with classification and ground point filtering.
The project is a joint collaboration between the Department of Prehistoric and Historical Archaeology, University of Vienna, the 3D Geospatial Data Processing Research Group at Heidelberg University, the Scientific Software Center of the Interdisciplinary Center for Scientific Computing (IWR) at Heidelberg University and the Institute for Prehistory, Protohistory and Near Eastern Archaeology, Heidelberg University.
The software development work described in this manuscript was carried out by the Scientific Software Center (SSC) of Heidelberg University in the framework of the project ‘Human-in-the-Loop Adaptive Terrain Filtering of 3D Point Clouds for Archaeological Prospection’ led by Maria Shinoto. The Scientific Software is funded as part of the Excellence Strategy of the German Federal and State Governments.
(Text: Sylvia Pscheidl)