Identifying wind turbines from multiresolution and multibackground remote sensing imagery

The wind energy industry has expanded in recent years. Promotion of the Sustainable Development Goals (SDGs) is expected to further increase the scale of the wind energy industry. Determining the location and quantity of wind turbines is crucial for monitoring the development status of the wind energy industry and evaluating wind energy production. In this study, we propose a method for simultaneously detecting and positioning wind turbines in remote sensing images, namely, Wind Turbine YOLO (WT-YOLO), based on the You Only Look Once version 5 model (YOLOv5). The wind turbine hub, base, and shadow hub are treated as key points in the proposed method. Regression terms are incorporated into the head of the YOLOv5 basic framework to predict the location of these three key points. The base point is utilized to determine the exact position of the wind turbine. A multibackground and multiresolution wind-turbine image dataset is constructed by sampling high-resolution images from Google Earth. The WT-YOLO method outperforms existing methods in both wind turbine detection and positioning on different types of land cover backgrounds and multiresolution images of the constructed dataset. In the spatial resolution range of 0.6 m to 5.4 m, WT-YOLO exhibits enhanced wind-turbine detection, where the average precision (AP) is 5.92 % to 15.43 % higher than that of existing wind-turbine detection methods. Wind-turbine positioning by WT-YOLO has a mean distance error (MDE) that is 16.06 m to 21.59 m lower than that of existing wind-turbine positioning methods. A comparative analysis showed that the shadow and the three key points are effective features for wind-turbine detection. The proposed WT-YOLO model can support detection, positioning and counting for wind turbines worldwide.