The land remote sensing wind turbine dataset has 1270 remote sensing images and contains 4459 individual wind turbines. The images are taken over a large time span and contain remote sensing images of the same wind farm at different times. The dataset has both YOLO and VOC tagging formats. The dataset can be divided into five categories based on the land background: sandy land, forest land, grassland, snow land, and wasteland. Rich land background can improve the robustness of the model, and different marker formats and a large number of wind turbine individuals can meet the object detection of different models. Affected by the size of different power wind turbines, the multi-angle imaging characteristics of remote sensing satellites, the different solar radiation angles in different seasons and vegetation shading, wind turbines show large differences in the images. The image features of the wind turbine shadow are more obvious than those of the wind turbine body, so in order for the detection model to better identify the wind turbine, we label the wind turbine body and the wind turbine shadow as a whole when using the labelImg tool for labeling the wind turbine target.