Mapping Wind Turbine Distribution in Forest Areas of China Using Deep Learning Methods

Abstract

Wind power plays a pivotal role in the achievement of carbon peaking and carbon neutrality. Extensive evidence has demonstrated that there are adverse impacts of wind power expansion on natural ecosystems, particularly on forests, such as forest degradation and habitat loss. However, incomplete and outdated information regarding onshore wind turbines in China hinders further systematic and in-depth studies. To address this challenge, we compiled a geospatial dataset of wind turbines located in forest areas of China as of 2022 to enhance data coverage from publicly available sources. Utilizing the YOLOv10 framework and high-resolution Jilin-1 optical satellite images, we identified the coordinates of 63,055 wind turbines, with an F1 score of 97.64%. Our analysis indicated that a total of 16,173 wind turbines were situated in forests, primarily within deciduous broadleaved forests (44.17%) and evergreen broadleaved forests (31.82%). Furthermore, our results revealed significant gaps in data completeness and balance in publicly available datasets, with 48.21% of the data missing and coverage varying spatially from 28.96% to 74.36%. The geospatial dataset offers valuable insights into the distribution characteristics of wind turbines in China and could serve as a foundation for future studies.

Keywords

• renewable energy
• wind power
• remote sensing
• keypoint detection