Identifying bare-earth or ground returns within point cloud data is a crucially important process for archaeologists who use airborne LiDAR data, yet there has thus far been very little comparative assessment of the available archaeology-specific methods and their usefulness for archaeological applications. This article aims to provide an archaeology-specific comparison of filters for ground extraction from airborne LiDAR point clouds. The qualitative and quantitative comparison of the data from four archaeological sites from Austria, Slovenia, and Spain should also be relevant to other disciplines that use visualized airborne LiDAR data. We have compared nine filters implemented in free or low-cost off-the-shelf software, six of which are evaluated in this way for the first time. The results of the qualitative and quantitative comparison are not directly analogous, and no filter is outstanding compared to the others. However, the results are directly transferable to real-world problem-solving: Which filter works best for a given combination of data density, landscape type, and type of archaeological features? In general, progressive TIN (software: lasground_new) and a hybrid (software: Global Mapper) commercial filter are consistently among the best, followed by an open source slope-based one (software: Whitebox GAT). The ability of the free multiscale curvature classification filter (software: MCC-LIDAR) to remove vegetation is also commendable. Notably, our findings show that filters based on an older generation of algorithms consistently outperform newer filtering techniques. This is a reminder of the indirect path from publishing an algorithm to filter implementation in software.
This is an open access article distributed under the Creative Commons Attribution License
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited