The quality and integrity of available dam data are critical for broad efforts to produce fine-scale assessments of their basin cycle, water quality, and other environmental and ecological effects. This study proposes a dam identification method in broad areas, object identification based on remote sensing images, and geographical analysis. First, we extracted dam candidate regions from broad surface water raster data at a spatial resolution of 30 m. Second, we trained and adjusted the multi-target recognition models using the dam sample from Google images, scanning dam candidate regions and extracting highly confidential dam positions. Moreover, we analyzed the location characteristics of the dams and used three geographical constraints to reduce background region overestimation further. The proposed framework was tested across an area of 13 265 km² (Aomori, Kanagawa, and Okinawa) and yielded promising results, which reduced the candidate areas to 13.43% of the total water area. We validate the framework results using the available high-resolution historical image series available on Google Earth. The framework recalled 112 dams at a rate of 91.06%, with a precision rate of 80%. We simultaneously identified 39 dams that were not recorded in the known datasets. Our results reveal that the overall framework is reliable for automatic and rapid dam detection with a foundation of open geographic products. The framework proposed in this paper is the new attempt to combine deep learning target detection technology and spatial analysis with dam identification in broad areas.

可用大坝数据的质量和完整性对​​于对其流域循环、水质和其他环境和生态影响进行精细评估的广泛努力至关重要。本研究提出了一种大面积大坝识别方法、基于遥感图像的物体识别方法和地理分析方法。首先，我们以 30 m 的空间分辨率从广阔的地表水栅格数据中提取大坝候选区域。其次，我们使用来自谷歌图像的大坝样本，扫描大坝候选区域并提取高度机密的大坝位置来训练和调整多目标识别模型。此外，我们分析了大坝的位置特征，并使用三个地理约束来进一步减少背景区域的高估。²（青森、神奈川和冲绳）并取得了令人鼓舞的结果，将候选区域减少到总水域的 13.43%。我们使用 Google Earth 上可用的高分辨率历史图像系列验证框架结果。该框架以 91.06% 的率召回了 112 座水坝，准确率为 80%。我们同时确定了 39 个未记录在已知数据集中的大坝。我们的结果表明，在开放地理产品的基础上，整体框架对于自动和快速的大坝检测是可靠的。本文提出的框架是将深度学习目标检测技术和空间分析与大范围大坝识别相结合的新尝试。