Decadal changes and influencing factors of global industrial heat sources

• Achieved 95.38 % accuracy and 94.38 % precision in industrial hotspot classification. • First global multi-class dataset of industrial heat sources (2013–2023). • Oil & gas platforms increased, coal chemical plants declined, others stable or moderate growth. • COVID-19 pandemic caused short-term disruptions in industrial activities. • Economic, demographic, and development factors strongly shape spatial distribution. Industrial heat sources are major contributors to greenhouse gas emissions, environmental degradation, and energy transitions, yet systematic global monitoring has been hindered by fragmented and sector-specific datasets. Here, we present a deep learning framework applied to high-resolution satellite imagery to automatically classify six major categories of industrial facilities—cement plants, steel plants, chemical plants, oil and gas development platforms, coal chemical plants, and open-pit mines. Based on this approach, we generate the first annual global dataset of industrial heat sources from 2013 to 2023. Our method achieves an overall classification accuracy of 95.38 % and an average precision of 94.38 %, with efficiency gains exceeding 98 % compared to traditional manual surveys. The results reveal distinct decadal trends: oil and gas platforms steadily increased, coal chemical plants declined markedly, cement plants and open-pit mines remained stable, while steel and chemical plants exhibited moderate growth. Correlation analysis further shows that economic development, population, and development stage strongly shape the spatial distribution of industrial activity. In addition, the dataset captures short-term disruptions, such as reduced activity during the COVID-19 pandemic. This study provides a reproducible and scalable foundation for analyzing industrial transitions, advancing global carbon accounting, and informing environmental and energy policy.