A Dataset of Medium-Resolution Industrial Heat Source in Beijing-Tianjin-Hebei Region

The rapid development of heavy industry and industrial expansion in the Beijing-Tianjin-Hebei region pose a significant threat to the sustainable development of the regional socio-economic system, while simultaneously exacerbating the burden on resources and the environment. Therefore, it is imperative to adopt effective measures to alleviate pressures, promote the transformation of the regional economy towards a green and low-carbon model, and ensure long-term sustainable development. This study utilized SDGSAT-1 TIS nighttime thermal infrared data and Landsat 8/9 OLI data to extract features of multiple industrial heat sources. A Support Vector Machine (SVM) algorithm was employed to construct a model for identifying industrial heat source production areas. By applying this model to the medium-resolution industrial heat source objects in the Beijing-Tianjin-Hebei urban agglomeration, 793 potential industrial heat source targets were identified. By overlaying Google Earth and POI (Point of Interest) data and subsequently conducting manual visual verification of the identification results, 748 of these were confirmed as actual industrial heat sources, achieving an identification accuracy of 94.32%.The 748 real industrial heat source objects were classified into 60 cement plants, 130 steel plants, 30 coal chemical plants, 121 oil and gas development plants, 53 thermal power plants, and 354 other types to form a medium-resolution industrial heat source dataset for the Beijing-Tianjin-Hebei region.Compared with existing publicly available industrial heat source datasets, this study shows significant improvement in the number of identifications, fineness and spatial coverage.The dataset provides information on the administrative region, land area, factory/mining name, category code, etc., of the industrial heat source. This dataset provides essential scientific evidence for optimizing the industrial structure, energy management, and efficient resource utilization in the region.