A dataset of medium-resolution industrial heat sources in the Beijing-Tianjin-Hebei region

<p indent="0mm">The rapid development of heavy industry and industrial expansion in the Beijing-Tianjin-Hebei region pose significant challenges to the sustainable development of the regional socio-economic system, while simultaneously intensifying pressures on resources and the environment. Therefore, it is imperative to adopt effective measures to alleviate these pressures, promote a transition toward a green and low-carbon economy, 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. Applying this model to the medium-resolution industrial heat source objects in the Beijing-Tianjin-Hebei urban agglomeration resulted in the identification of 793 potential industrial heat source targets. By overlaying Google Earth imagery and Point of Interest (POI) data, followed by manual visual verification, 748 targets were confirmed as actual industrial heat sources, yielding an identification accuracy of 94.32%. These confirmed industrial heat source objects were further classified into six categories: cement plants (60), steel plants (130), coal chemical plants (30), oil and gas development plants (121), thermal power plants (53), and other types (354). Based on these results, a medium-resolution industrial heat source dataset for the Beijing-Tianjin-Hebei region was established. Compared with existing publicly available industrial heat source datasets, this study shows substantial improvements in identification quantity, and spatial coverage. The dataset provides key attributes for each industrial heat source, including administrative region, land area, factory/mining name, category code, etc. This dataset provides essential scientific support for regional industrial structure optimization, energy management, and the efficient resource utilization in the region.