Deep Learning and Hydrological Feature Constraint Strategies for Dam Detection: Global Application to Sentinel-2 Remote Sensing Imagery

Dams are instrumental in flood and drought control, agricultural irrigation, and hydropower generation. Remote sensing imagery enables the detection of dams across extensive areas, thereby supplying valuable data to facilitate effective water resource management. However, existing dam detection methods cannot achieve high-precision and rapid detection of dams in medium-resolution remote sensing images at the global scale. To fill the gap, deep learning and hydrological feature constraint strategies (DL-HFCS) for dam detection in Sentinel-2 MSI imagery were proposed. This method leverages the efficient YOLOv5s model for preliminary deep learning-based dam detection. Next, based on the hydrological features of dams, constraints such as adjacent water body, single reservoir-based dam number, watershed river network, and detection box-based river network elevation difference are progressively introduced to eliminate false detections. To verify the effectiveness and generalization of our method, 91 1° × 1° regions worldwide were selected as test areas to conduct dam prediction experiments. Experimental results demonstrate that the DL-HFCS achieves a precision of 86.29% and a recall of 82.26%, a 47.58% improvement in precision compared to deep learning alone. Furthermore, over 98% of the detection results accurately locate the dam bodies, whereas in existing dam datasets, this proportion is less than 75%. This study indicates that the HFCS can effectively reduce the false alarm in dam detection. The DL-HFCS method enables thorough and accurate dam detection on a global scale. It holds significant potential for application to Sentinel-2 MSI imagery worldwide, thereby facilitating the creation of a global dam dataset.