Moated sites are crucial for revealing the formation of early civilizations and societies in Southeast Asia, and a significant amount of effort has been expended in investigating their distribution. This work is the first application of deep learning object detection methods to identify moated sites from time series satellite images. We presented multi-information fusion data (N-RGB) based on the fusion of multispectral and vegetation indices from Sentinel-2 time series imagery, generated a dataset of moated sites via the data augmentation method, and improved the YOLOv5s model by adding bidirectional feature pyramid network (BiFPN) structures for automatically identifying moated sites. The results indicate that the model trained with time series N-RGB data improves precision, recall, and mAP by more than 20.0% compared with single image data. The improved model was able to enhance the identification of small, moated sites and achieved 100% detection in a test of 100 moated sites. Ultimately, 629 targets were detected in northeast Thailand, with a false-negative rate of less than 3%, and 116 probable sites were identified. Among these, 6 probable sites were highly likely to be moated sites, as visually verified by high-resolution GEE imagery. In addition, among the targets automatically detected in other regions of continental Southeast Asia, the 5, 3, 2, 1, and 7 most probable sites were identified in Cambodia, Myanmar, Laos, Vietnam and other regions of Thailand, respectively. In summary, our approach enables the automatic detection of exposed and visible moated sites from satellite imagery, and could improve site discovery and documentation capabilities, opening new perspectives in larger geographic site units and even in civilization surveys.

中文翻译：

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在泰国东北部使用时间序列卫星图像和改进的深度学习模型进行护城河站点对象检测


护城河遗址对于揭示东南亚早期文明和社会的形成至关重要，人们在调查其分布方面花费了大量精力。这项工作是深度学习对象检测方法的首次应用，用于从时间序列卫星图像中识别护城河站点。我们基于 Sentinel-2 时间序列图像的多光谱和植被指数融合提出了多信息融合数据 （N-RGB），通过数据增强方法生成了护城河站点数据集，并通过添加双向特征金字塔网络 （BiFPN） 结构来改进 YOLOv5s 模型用于自动识别护城河站点。结果表明，与单个图像数据相比，使用时间序列 N-RGB 数据训练的模型将精度、召回率和 mAP 提高了 20.0% 以上。改进的模型能够增强对小型护城河站点的识别，并在 100 个护城河站点的测试中实现了 100% 的检出率。最终，在泰国东北部检测到 629 个靶点，假阴性率低于 3%，并确定了 116 个可能的位点。其中，6 个可能的地点极有可能是护城河遗址，高分辨率 GEE 图像的目测验证。此外，在东南亚大陆其他地区自动探测到的目标中，柬埔寨、缅甸、老挝、越南和泰国其他地区分别确定了 5、3、2、1 和 7 个最可能的地点。总之，我们的方法能够从卫星图像中自动检测暴露和可见的护城河遗址，并且可以提高遗址发现和记录能力，为更大的地理遗址单位甚至文明调查开辟新的视角。