Human-in-the-loop development of spatially adaptive ground point filtering pipelines: an archaeological case study
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 georefe...
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| Main Authors: | , , , , |
|---|---|
| Format: | Article (Journal) |
| Language: | English |
| Published: |
October/December 2022
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| In: |
Archaeological prospection
Year: 2022, Volume: 29, Issue: 4, Pages: 503-524 |
| ISSN: | 1099-0763 |
| DOI: | 10.1002/arp.1873 |
| Online Access: | Verlag, kostenfrei, Volltext: https://doi.org/10.1002/arp.1873 Verlag, kostenfrei, Volltext: https://onlinelibrary.wiley.com/doi/abs/10.1002/arp.1873 
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| Author Notes: | Michael Doneus, Bernhard Höfle, Dominic Kempf, Gwydion Daskalakis, Maria Shinoto |
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| 520 | |a 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. In this paper, we are focussing on a special workflow for optimal classification results in these heterogeneous environments, which integrates expert knowledge. We present a novel Python-based 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. | ||
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