Mapping global onshore wind turbines using multi-source remote sensing images and hybrid learning approaches

We developed an integrated framework that combines OpenStreetMap (OSM) data with multi-source remote sensing data (Google Earth and Sentinel-1/2) and traditional machine learning models (ResNet-18 and Random Forest) to map global onshore wind turbines. This dataset facilitates essential studies on renewable energy land management, ecological impact analysis, and data-driven energy transition policies. The dataset includes: 1. A comprehensive global inventory of 379,595 onshore wind turbines in the format of a geospatial shapefile. The dataset includes geolocation coordinates for all wind turbines, along with corresponding nation (Field: 'Nation') and land use classification (Field: 'landtype') for each wind turbine. 2. The dataset comprises 37,285 carefully annotated 256×256 pixel Google Earth image patches, containing both positive (wind turbine) and negative (background) samples, and is organized into folders with training (60%, 22,372 images) and testing sets (20%, 7,457 images) validating sets (20%, 7,456 images). The images could serve as foundational data for training deep learning models in wind turbine classification, segmentation, and detection tasks. The code file includes: 1. A PyTorch-based ResNet-18 implementation for classifying onshore wind turbines in Google Earth images, including codes for model architecture and pre-trained weights. 2. The GEE-based code for the Random Forest model, including sample point splitting (training/test sets) and model training.本研究构建了一套融合开放街道地图（OpenStreetMap, OSM）数据、多源遥感数据（谷歌地球与Sentinel-1/2）以及传统机器学习模型（ResNet-18与随机森林（Random Forest））的集成框架，用于全球陆上风力涡轮机制图。该数据集可为可再生能源土地管理、生态影响分析以及数据驱动的能源转型政策等核心研究提供重要支撑。 数据集包含： 1. 一套涵盖379595台陆上风力涡轮机的全球综合名录，格式为地理空间形状文件（geospatial shapefile）。该数据集包含所有风力涡轮机的地理坐标，以及每台涡轮机对应的所属国家（字段：'Nation'）与土地利用分类信息（字段：'landtype'）。 2. 数据集包含37285张经精细标注的256×256像素谷歌地球影像斑块，涵盖正样本（风力涡轮机）与负样本（背景区域），并按文件夹划分为训练集（60%，22372张图像）、测试集（20%，7457张图像）与验证集（20%，7456张图像）。此类影像可作为风力涡轮机分类、分割与检测任务的深度学习模型训练基础数据。 代码文件包含： 1. 基于PyTorch的ResNet-18实现代码，用于对谷歌地球影像中的陆上风力涡轮机进行分类，涵盖模型架构代码与预训练权重相关代码。 2. 基于谷歌地球引擎（Google Earth Engine, GEE）的随机森林模型代码，包含样本点拆分（训练/测试集）与模型训练相关功能。