首页|Identifying reservoirs in northwestern Iran using high-resolution satellite images and deep learning

Identifying reservoirs in northwestern Iran using high-resolution satellite images and deep learning

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Reservoirs play a critical role in terrestrial hydrological systems,but the contribution of small and medium-sized ones is rarely considered and recorded.Particularly in developing countries,there is a rapid increase of such reservoirs due to their quick construction.Accurately identify-ing these reservoirs is important for understanding social and economic development,but distinguishing them from other natural water bodies poses a significant challenge.Thus,we propose a method to identify reservoirs using high-resolution satellite images and deep learning algorithms.We trained models with various parameters and network structures,and You Only Look Once version 7(YOLOv7)outperformed other algorithms and was selected to build the final model.The method was applied to a region in northwestern Iran,characterized by an abundance of reservoirs of various sizes.Evaluation results indicated that our method was highly accurate(mAP:0.79,Recall:0.76,Precision:0.82).The YOLOv7 model was able to automatically identify 650 reservoirs in the entire study region,indicating that the proposed method can accurately detect reservoirs and has the potential for broader-scale surveys,even global applications.

Reservoirdeep learningobject detectionIran

Kaidan Shi、Yanan Su、Jinhao Xu、Yijie Sui、Zhuoyu He、Zhongyi Hu、Xin Li、Harry Vereecken、Min Feng

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College of Earth and Environmental Sciences,Lanzhou University,Lanzhou,China

National Tibetan Plateau Data Center,State Key Laboratory of Tibetan Plateau Earth System,Environment and Resources,Institute of Tibetan Plateau Research,Chinese Academy of Sciences,Beijing,China

University of Chinese Academy of Sciences,Beijing,China

School of Water Resources and Environment,China University of Geosciences,Beijing,China

Institute of Bio-and Geosciences,Jülich,Germany

Academy of Plateau Science and Sustainability,Qinghai Normal University,Xining,China

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2024

10.1080/10095020.2024.2358892

地球空间信息科学学报(英文版)
武汉大学（原武汉测绘科技大学）

地球空间信息科学学报(英文版)

影响因子：0.207
ISSN：1009-5020
年,卷(期)：2024.27(3)