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<p>Hydraulic infrastructures play significant roles in societal and civilization development. Traditional archaeological methods face challenges in identifying ancient dams, as most of them were damaged and blended with the natural environment. For the first time, this study focuses on the intelligent identification of ancient dams surrounding the Liangzhu Ancient City in the Hangjiahu Plain, China, utilizing historical satellite and aerial imagery of 1940s-1970s and deep learning techniques. After comparing models of Random Forest, Faster R-CNN, YOLOv5, YOLOv8 and YOLOv11, the YOLOv11 was chosen. The model was optimized by the GIoU, the Convolutional Block Attention Module and an additional detection layer to improve small target recognition and reduce misidentifications. The optimized YOLOv11 model achieved a recall rate of 68% and an precision rate of 65%, which significantly enhances speed and efficiency of ancient dam identification compared to traditional archaeological methods, providing a flexible and accurate tool for large-scale surveys of ancient water facilities.</p>

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Machine learning-based identification of ancient water management facilities in Liangzhu, China

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年10月14日

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  • Yiran Wang,
  • Shaochun Dong,
  • Yixin Zhang,
  • Hongwei Yin,
  • Tao Zhang

摘要

Hydraulic infrastructures play significant roles in societal and civilization development. Traditional archaeological methods face challenges in identifying ancient dams, as most of them were damaged and blended with the natural environment. For the first time, this study focuses on the intelligent identification of ancient dams surrounding the Liangzhu Ancient City in the Hangjiahu Plain, China, utilizing historical satellite and aerial imagery of 1940s-1970s and deep learning techniques. After comparing models of Random Forest, Faster R-CNN, YOLOv5, YOLOv8 and YOLOv11, the YOLOv11 was chosen. The model was optimized by the GIoU, the Convolutional Block Attention Module and an additional detection layer to improve small target recognition and reduce misidentifications. The optimized YOLOv11 model achieved a recall rate of 68% and an precision rate of 65%, which significantly enhances speed and efficiency of ancient dam identification compared to traditional archaeological methods, providing a flexible and accurate tool for large-scale surveys of ancient water facilities.