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Monitoring the resumption of work and production (RWP) is of great significance to the restoration of social order and the economy after the coronavirus disease 2019 (COVID‐19) epidemic. In this study, the most critical period of epidemic prevention and control and economic recovery was divided into four phases according to the changes in emergency response levels during the epidemic in China, and based on VIIRS I‐Band 375‐m active fire data (VNP14IMG), NO2 tropospheric vertical column density (TVCD) data, and intracity travel intensity data, the density‐based spatial clustering of applications with the noise logistic regression industrial heat source identification model, NO2 difference model, and urban relative recovery index were constructed to analyze work resumption at different phases. The results indicate that: (1) the production intensity of China's heat source enterprises decreased first and then exceeded the epidemic period compared with the same period in history. From phases 1 to 3, the heat source enterprise production intensity was lower than the same period in 2019 (the production intensities were 19,562.79, 22,462.57, and 38,466.89, respectively), and the reduction rates of production intensity were 27.5, 26.1, and 22.7%, respectively. In phase 4, enterprise production intensity achieved an increase of 7%. (2) The process of the RWP in China is closely related to NO2 TVCD. In the first three phases, significantly decreased emission sources of NO2 reduced the total value of NO2 by varying degrees in most provinces of the country. Correspondingly, in phase 4, the NO2 TVCD in some provinces recovered to the value of the same period in 2019, including typical industrial bases (Shandong, Hebei, Shanxi, etc.) and economically developed coastal areas (Jiangsu, Guangdong, Zhejiang, etc.). (3) In phase 3, the intracity travel intensity in all cities improved, and the city relative recovery index (RRI) rose across the board. In particular, the western cities were better than the eastern cities. Among them, the RRIs of most cities in southwest China (Sichuan, Yunnan) and northwest China (Gansu) were more than 90%. Notably, the recovery situation exhibited an obvious “siege phenomenon,” and the recovery of core cities required a longer period. Overall, the sudden COVID‐19 epidemic seriously affected the production of Chinese enterprises. However, benefitting from rapid and effective epidemic prevention and control measures, the resumption of work and production has been a success. Our study provides guidance for economic recovery strategies during post‐pandemic stages.

As a result of limited resources and economic development acting as dual bottleneck constraints, optimizing industrial layout structures has been a general trend in heavy industry. The visual supervision of heat source factories based on integrated multidimensional information is important for optimizing an industrial layout. Based on Visible Infrared Imaging Radiometer Suite (VIIRS) I‐band 375‐m active fire product (VNP14IMG) data, point of interest data, enterprise attribute information and other data, combined with clustering regression, knowledge graphs (KGs), 3D geographic information systems, and other technical methods, the temporal and spatial variations in China's heat source industries are macroscopically analyzed, and a visual supervision platform for heat source industries with functions such as visualization, time‐series analysis, and knowledge discovery is established. The results show that: (1) overall, heat source factories exhibit a spatial pattern of dense in the east and sparse in the west, and the number of industrial heat source objects and the number of industrial fire hotspots decreased from 2013 to 2021, with rates of decline of 22.0 and 27.3%, respectively; (2) the enterprise KG, which contains basic enterprise information, dynamic enterprise risk information and enterprise equity structure information, can provide users with accurate and reliable enterprise knowledge; and (3) the remote sensing monitoring information platform for heat source factories performs well in terms of the discovery and management of heat source factories at large scale. In general, the platform constructed in this study can support the rapid monitoring and positioning of industrial heat sources over large areas to improve supervision in terms of finding problems and preventing risks and to provide a necessary reference for optimizing industrial spatial patterns and environmental governance.