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

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Názov: A detection-screening framework for karez (ancient underground irrigation system) using deep learning and geospatial analysis.
Autori: Ilniyaz, Osman, Zhang, Yong, Wang, Long, Zhang, Xiaohe, Kurban, Alishir, Eziz, Anwar, Ablimit, Kahar, Bourgeois, Jean, Barbaix, Sophie, Van de Voorde, Tim, Jiang, Jinguo, Xiang, Xianbiao, Wang, Yumiao
Zdroj: npj Heritage Science; 8/30/2025, Vol. 13 Issue 1, p1-15, 15p
Predmety: ARID regions, GROUNDWATER, REMOTE-sensing images, HIERARCHICAL clustering (Cluster analysis), WATER management
Abstrakt: 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. [ABSTRACT FROM AUTHOR]
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Databáza: Complementary Index
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Abstrakt: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. [ABSTRACT FROM AUTHOR]
ISSN:20507445
DOI:10.1038/s40494-025-01967-6