Test data supporting the paper 'Interactive, shallow machine-learning based semantic segmentation of 2D and 3D geophysical data from archaeological sites'

This repository comprises three geophyiscal test datasets supporting the paper 'Interactive, shallow machine-learning based semantic segmentation of 2D and 3D geophysical data from archaeological sites' by Lieven Verdonck, Michel Dabas and Marc Bui, Remote Sensing, 2025, 17, 3092, https://www.mdpi.com/2072-4292/17/17/3092. Technological developments in archaeological geophysics have led to growing data volumes, so that an important bottleneck is now at the stage of data interpretation. Manual delineation and classification of the significant anomalies are time-consuming. In the abovementioned paper, we describe how shallow machine learning (random forests) can be used to interpret near-surface geophysical data from archaeological sites. We show their potential by applying them to the three datasets in this Zenodo repository, and discuss the limitations and possible further improvements. Please see the instructions in each zip-folder for more information on how to use the data. For information on the provenance of the data and on the methodology of data acquisition and processing, see the paper and the supplementary data.For the software used to perform the machine-learning based semantic segmentation, see also https://github.com/lrverdon/Shallow-Machine-Learning-for-Archaeological-Geophysics.本仓库包含三套地球物理测试数据集，用于支撑Lieven Verdonck、Michel Dabas与Marc Bui于2025年发表在《Remote Sensing》（MDPI出版社）的论文"Interactive, shallow machine-learning based semantic segmentation of 2D and 3D geophysical data from archaeological sites"，论文链接：https://www.mdpi.com/2072-4292/17/17/3092。 考古地球物理学的技术进步带来了数据体量的持续增长，当前数据解译环节已成为制约研究推进的关键瓶颈。对显著地球物理异常进行人工勾画与分类，需耗费大量时间成本。在上述论文中，我们阐述了如何利用浅层机器学习（shallow machine learning，即随机森林(random forests)）解译考古遗址的近地表地球物理数据。通过将该方法应用于本Zenodo仓库中的三套数据集，我们验证了其应用潜力，并讨论了该方法的局限性与潜在改进方向。 如需了解数据的具体使用方法，请查阅每个压缩文件夹内的操作说明。 关于数据来源、数据采集与处理的详细方法论，请参阅原论文及补充材料。 如需获取用于实现本研究中基于机器学习的语义分割的相关软件代码，请访问：https://github.com/lrverdon/Shallow-Machine-Learning-for-Archaeological-Geophysics。