基于卫星的考古遗址盗掘检测使用机器学习
Satellite-Based Detection of Looted Archaeological Sites Using Machine Learning
AI总结：
利用机器学习和卫星图像检测考古遗址盗掘，通过CNN和传统ML方法对比，实现高精度识别
摘要：
考古遗址的盗掘对文化遗产构成严重威胁，但监控成千上万的偏远地点在操作上仍然具有挑战性。我们提出了一种可扩展且基于卫星的流程，用于检测被掠夺的考古遗址，利用PlanetScope每月拼接图（4.7米/像素）和一个经过精心编写的阿富汗考古遗址数据集（898个被掠夺、1045个保存的遗址）的多年影像（2016-2023年）和遗址足迹掩膜。我们比较了（i）端到端的CNN分类器，其在原始RGB块上训练，以及（ii）传统机器学习（ML）在手工制作的光谱/纹理特征和最近的遥感基础模型的嵌入上训练。结果表明，ImageNet预训练的CNN结合空间掩膜达到F1得分为0.926，明显超过了最强的传统ML设置，其使用SatCLIP-V+RF+Mean，即位置和视觉嵌入输入随机森林，再通过基于均值的时间聚合。消融研究显示，ImageNet预训练（即使在存在领域偏移的情况下）和空间掩膜提高了性能。相比之下，地理空间基础模型嵌入与手工制作的特征表现相当，表明掠夺特征非常局部化。该存储库可在https://github.com/microsoft/looted_site_detection上获得。
英文摘要：
Looting at archaeological sites poses a severe risk to cultural heritage, yet monitoring thousands of remote locations remains operationally difficult. We present a scalable and satellite-based pipeline to detect looted archaeological sites, using PlanetScope monthly mosaics (4.7m/pixel) and a curated dataset of 1,943 archaeological sites in Afghanistan (898 looted, 1,045 preserved) with multi-year imagery (2016--2023) and site-footprint masks. We compare (i) end-to-end CNN classifiers trained on raw RGB patches and (ii) traditional machine learning (ML) trained on handcrafted spectral/texture features and embeddings from recent remote-sensing foundation models. Results indicate that ImageNet-pretrained CNNs combined with spatial masking reach an F1 score of 0.926, clearly surpassing the strongest traditional ML setup, which attains an F1 score of 0.710 using SatCLIP-V+RF+Mean, i.e., location and vision embeddings fed into a Random Forest with mean-based temporal aggregation. Ablation studies demonstrate that ImageNet pretraining (even in the presence of domain shift) and spatial masking enhance performance. In contrast, geospatial foundation model embeddings perform competitively with handcrafted features, suggesting that looting signatures are extremely localized. The repository is available at https://github.com/microsoft/looted_site_detection.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
