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<p>Karez, an ancient engineering marvel, utilizes gravity to transport underground water to the surface without external power. Typically, a karez comprises numerous shafts (vertical wells), and traditional mapping methods are both time-consuming and labor-intensive. To address these challenges, this study developed an integrated detection-screening framework for karez mapping. The karez shafts were detected by using high spatial resolution satellite imagery and deep learning architectures (Faster-RCNN, SSD, YoloV3, and MMDetection). Subsequently, a directed fan-shaped buffering method, combined with hierarchical clustering, was introduced to filter out misidentified shaft-like structures. Results showed that the MMDetection outperformed other models, achieving a mean average precision (mAP50-95) of 0.833. Field validation confirmed that the screening methods eliminated 90.20% of false shaft detections. This study has obtained the largest number of karez shafts to date in the study area, while providing a transferable technical framework for global applications in cultural heritage documentation and arid land water management.</p>

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A detection-screening framework for karez (ancient underground irrigation system) using deep learning and geospatial analysis

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年8月30日

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  • Osman Ilniyaz,
  • Yong Zhang,
  • Long Wang,
  • Xiaohe Zhang,
  • Alishir Kurban,
  • Anwar Eziz,
  • Kahar Ablimit,
  • Jean Bourgeois,
  • Sophie Barbaix,
  • Tim Van de Voorde,
  • Jinguo Jiang,
  • Xianbiao Xiang,
  • Yumiao Wang

摘要

Karez, an ancient engineering marvel, utilizes gravity to transport underground water to the surface without external power. Typically, a karez comprises numerous shafts (vertical wells), and traditional mapping methods are both time-consuming and labor-intensive. To address these challenges, this study developed an integrated detection-screening framework for karez mapping. The karez shafts were detected by using high spatial resolution satellite imagery and deep learning architectures (Faster-RCNN, SSD, YoloV3, and MMDetection). Subsequently, a directed fan-shaped buffering method, combined with hierarchical clustering, was introduced to filter out misidentified shaft-like structures. Results showed that the MMDetection outperformed other models, achieving a mean average precision (mAP50-95) of 0.833. Field validation confirmed that the screening methods eliminated 90.20% of false shaft detections. This study has obtained the largest number of karez shafts to date in the study area, while providing a transferable technical framework for global applications in cultural heritage documentation and arid land water management.