Industrial heat source (IHS) radiation areas are key accumulation zones for fine particulate matter with an aerodynamic diameter of 2.5 μm or less (PM2.5) in heavily industrialized regions, but PM2.5 concentration reduction rates by vegetation types have not been systematically assessed. A systematic analysis of PM2.5 concentration reduction rates by vegetation types using IHS, PM2.5, land-cover, and digital elevation model (DEM) data was conducted to assess PM2.5 concentration reduction rates by vegetation types within IHS radiation areas. First, this study adopted the published IHS radiation-area dataset developed by Xin Sui et al. to define the spatial extent of industrially influenced areas. Second, PM2.5 concentrations were extracted within IHS radiation areas and areas covered by different vegetation types to support the calculation of PM2.5 concentration reduction rates. Third, PM2.5 concentration reduction rates by vegetation types were evaluated through masking and regional statistical analysis. Results for Beijing–Tianjin–Hebei (BTH) in 2015 and 2020 show that: (1) the average PM2.5 decreased from 71.70 to 39.60 µg/m3, corresponding to an overall reduction of 44.8%; (2) PM2.5 concentration reduction rates varied substantially among vegetation types; open deciduous broadleaved forest showed the highest reduction rate of 39.08%, while rainfed and irrigated cropland showed negative reduction rates of −9.35% and −6.71%; (3) city-scale and case analyses show denser vegetation in radiation zones generally lowers PM2.5 even under ongoing industrial activity. The study supports vegetation greening, IHS control, regional air quality improvement, and sustainable industrial development strategies.

ABSTRACT As the world's largest cement producer, China urgently requires accurate and comprehensive datasets to support environmental policy development. However, existing datasets are outdated, limited in scope, and lack detailed information. In this study, a target cement plant (t‐cement) dataset in China was constructed using an improved YOLOv5‐IEG model and integrating multi‐source data to address these gaps. First, the improved YOLOv5‐IEG model, as established by Li et al. (2024), was implemented to determine the areas and locations of t‐cement plants, resulting in a comprehensive dataset of 953 records. Then, 23 detailed attributes including 11 production status attributes of each t‐cement plant were extracted based on long‐term NPP‐VIIRS Active Fire/Hotspot (ACF) products from 2012 to 2022, Point Of Interest (POI) data, and corporate enterprise information. Those attributes were used to analyse the production status of each target cement plant, along with its spatiotemporal trends. The analysis revealed that: (1) t‐cement plants in China (66.9%) are primarily concentrated east of the Hu Huanyong Line, while 29.9% are located within the Hu Huanyong Line region; only approximately 0.03% of cement plants are situated to the west of the line. (2) The number of operational cement plants increased annually from 2012 to 2016, but began to decline gradually from 2017 onward, with the majority of closures occurring in traditional industrial provinces such as Hebei, Shandong, and Shanxi. (3) Comparative analysis with other available datasets showed a 95% spatial overlap rate and 40% more records, demonstrating the dataset's superior accuracy and comprehensiveness. The dataset, which includes detailed temporal attributes, aligns with China's environmental policies and offers significant potential for applications in pollution control, emissions reduction, and sustainable development. This research provides a valuable resource for supporting informed decision‐making in China's environmental governance.

Cement serves as the cornerstone of urban development, yet faces increasing scrutiny due to its environmental impact, particularly in greenhouse gas emissions. This study addresses the urgent need for precise extraction of cement plants to promote green industrial development and environmental monitoring. Based on the U-Net model, we propose an enhanced method integrating the Canny edge detection algorithm and Convolutional Block Attention Module (CBAM) attention mechanism. Through comprehensive experimentation, our method demonstrates improvements compared to the baseline model, with an increase in accuracy by 1.14% and Intersection over Union (IoU) by 2.21%. Our findings highlight the potential of deep learning frameworks in mitigating resource dependence and time constraints while ensuring accurate localization of cement plants. This research lays a critical foundation for advancing sustainable industrial practices and environmental management.

In the past decade,India's industrialization process has advanced rapidly and has fully entered the stage of industrialization construction.Therefore,timely understanding of the industrialization development process in the Indian is of great significance for the country's continued industrial growth.Based on the 375-meter high spatiotemporal resolution NPP-VIIRS active fire/hotspot data,this study applies an improved K-means spatio-temporal density segmentation algorithm,combined with night-time light filtering,and overlays Google Earth high-resolution image data,resulting in a dataset of industrial heat sources in operation in India from 2012 to 202.After manual verification and analysis,the recognition accuracy of the dataset in the study is 93.32％,showing significant improvements in recognition accuracy,count,granularity,and spatial overlap,compared to the same period of data.The dataset provides key information,such as the four-corner latitude and longitude coordinates of industrial heat sources,fire point counts within the region,the administrative areas they belong to,and their operational start and end years.It can offer scientific and reliable data support for analyzing the industrialization process in Indian,and also provides certain decision-making support for the country's future development and for promoting Sino-Indian cooperation.

The rapid industrialization of the Yangtze River Delta (YRD) region as a major driver of economic growth; however, its intensive energy consumption and persistently high carbon emissions, has posed significant threats to the region’s sustainable development. An industrial heat sources (IHS) detection model based on support vector machine (SVM) using SDGSAT-1 TIS nighttime thermal infrared data and Landsat 9 OLI data was produced. Firstly, multiple IHS features including 4 thermal radiation indices and 3 optical features were extracted. Subsequently, an identification model for industrial heat production areas was constructed using SVM algorithm. Lastly, 748 IHS objects in YRD region were constructed and confirmed manually by overlaying Google Earth imagery, corresponding to an identification accuracy of 94.32%. This dataset provides an important scientific basis for industrial structure optimization, energy management, and efficient resource utilization in the YRD region.

Effective production, living, and ecological space allocation is essential for improving and optimizing urban space development. In this study, we proposed a production–living–ecological space (PLES) identification method based on Point of Interest (POI) data and China Land Cover Dataset (CLCD) to identify PLESs in Xuzhou City for the years 2012, 2018, and 2022, with an average recognition accuracy of 89.81%. Moreover, the land-use transfer matrix, center of gravity migration, and Geo-detector were used to reveal the spatiotemporal pattern evolution of PLESs. The results showed that: (1) The distribution of PLESs presented significant differentiation between Urban Built-Up Area (UBUA) and Non-Urban Built-Up Area (NUBUA). UBUA was mainly composed of living spaces, while NUBUA was primarily characterized by production–ecological spaces. (2) The intensive utilization of urban land led to an increase in the area of multifunctional spaces, while the complexity of urban space increased. (3) During 2012 to 2022, the center of gravity of PLESs remained relatively stable. The moving distances were all less than 1 km (except for ecological space from 2012 to 2018). (4) The evolution of PLESs was closely linked with socio-economic factors, and the interactions between the factors also had a significant driving effect on PLESs.

Industrial heat sources (IHSs) are major contributors to energy consumption and environmental pollution, making their accurate detection crucial for supporting industrial restructuring and emission reduction strategies. However, existing models either focus on single-class detection under complex backgrounds or handle multiclass tasks for simple targets, leaving a gap in effective multiclass detection for complex scenarios. To address this, we propose a novel multiclass IHS detection model based on the YOLOv8-FC framework, underpinned by the multiclass IHS training dataset constructed from optical remote sensing images and point-of-interest (POI) data firstly. This dataset incorporates five categories: cement plants, coke plants, coal mining areas, oil and gas refineries, and steel plants. The proposed YOLOv8-FC model integrates the FasterNet backbone and a Coordinate Attention (CA) module, significantly enhancing feature extraction, detection precision, and operational speed. Experimental results demonstrate the model’s robust performance, achieving a precision rate of 92.3% and a recall rate of 95.6% in detecting IHS objects across diverse backgrounds. When applied in the Beijing–Tianjin–Hebei (BTH) region, YOLOv8-FC successfully identified 429 IHS objects, with detailed category-specific results providing valuable insights into industrial distribution. It shows that our proposed multiclass IHS detection model with the novel YOLOv8-FC approach could effectively and simultaneously detect IHS categories under complex backgrounds. The IHS datasets derived from the BTH region can support regional industrial restructuring and optimization schemes.

Biomass and industrial fire points are crucial to forest fire prevention and industrial carbon emissions research. However, only a few investigations in the literature focus on fire point classification tasks using different spatial resolutions fire datasets in China. A comprehensive and accurate analysis of the spatial and temporal distribution characteristics and trends of fire points in China during the past decade is lacking. In this study, industrial heat source data, four fire point data, including VIIRS 750-m Nightfire (VNF), VIIRS 375-m Active Fire points (ACF), MODIS 1000-m fire points (MF) and Landsat-8 30 m fires data (LF), and land cover type data were integrated to more accurately classify fire points into biomass or industrial fire points. The result shows that our classification is more accurate than ACFu2019s results, and several conclusions were obtained from the spatial and temporal analysis of the total/biomass/industrial fire points across the four fire point datasets in China. (1) There was a high spatial and temporal correlation across all four datasets in China between 2012 and 2021. The annual numbers of fire points from the four fire point datasets all increased from 2012 to 2013, peaked in 2014, and have since declined. (2) The number of industrial fire points across the four datasets was static and persistent over the time series and had tight spatial aggregation, with little variation in their annual numbers and spatial distribution over time. In contrast, biomass fire points exhibited more significant changes in their spatial distribution, and the annual number declined after 2014. (3) The distribution of biomass fire points shifted northward over time, gradually moving from the Yangtze-Huai Plain and Yunnan Province in 2012 to northeastern China after 2018. These findings highlight the importance of considering temporal factors when analyzing fire point data, as well as the potential benefits of utilizing multiple datasets to achieve more accurate results.

Accurate land parcel segmentation in remote sensing imagery is critical for applications such as land use analysis, agricultural monitoring, and urban planning. However, existing methods often underperform in complex scenes due to small-object segmentation challenges, blurred boundaries, and background interference, often influenced by sensor resolution and atmospheric variation. To address these limitations, we propose a dual-stage framework that combines an enhanced YOLOv5s detector with the Segment Anything Model (SAM) to improve segmentation accuracy and robustness. The improved YOLOv5s module integrates Efficient Channel Attention (ECA) and BiFPN to boost feature extraction and small-object recognition, while Soft-NMS is used to reduce missed detections. The SAM module receives bounding-box prompts from YOLOv5s and incorporates morphological refinement and mask stability scoring for improved boundary continuity and mask quality. A composite Focal-Dice loss is applied to mitigate class imbalance. In addition to the publicly available CCF BDCI dataset, we constructed a new WuJiang dataset to evaluate cross-domain performance. Experimental results demonstrate that our method achieves an IoU of 89.8% and a precision of 90.2%, outperforming baseline models and showing strong generalizability across diverse remote sensing conditions.

Biomass and industrial fire points are crucial to forest fire prevention and industrial carbon emissions research. However, only a few investigations in the literature focus on fire point classification tasks using different spatial resolutions fire datasets in China. A comprehensive and accurate analysis of the spatial and temporal distribution characteristics and trends of fire points in China during the past decade is lacking. In this study, industrial heat source data, four fire point data, including VIIRS 750-m Nightfire (VNF), VIIRS 375-m Active Fire points (ACF), MODIS 1000-m fire points (MF) and Landsat-8 30 m fires data (LF), and land cover type data were integrated to more accurately classify fire points into biomass or industrial fire points. The result shows that our classification is more accurate than ACF’s results, and several conclusions were obtained from the spatial and temporal analysis of the total/biomass/industrial fire points across the four fire point datasets in China. (1) There was a high spatial and temporal correlation across all four datasets in China between 2012 and 2021. The annual numbers of fire points from the four fire point datasets all increased from 2012 to 2013, peaked in 2014, and have since declined. (2) The number of industrial fire points across the four datasets was static and persistent over the time series and had tight spatial aggregation, with little variation in their annual numbers and spatial distribution over time. In contrast, biomass fire points exhibited more significant changes in their spatial distribution, and the annual number declined after 2014. (3) The distribution of biomass fire points shifted northward over time, gradually moving from the Yangtze-Huai Plain and Yunnan Province in 2012 to northeastern China after 2018. These findings highlight the importance of considering temporal factors when analyzing fire point data, as well as the potential benefits of utilizing multiple datasets to achieve more accurate results.

With the global temperature rising, fires are becoming profound impact on Earth system. Accurate fire type identification based on remote sensing technology is becoming great significance for disaster prevention and reduction. In this study, we built a system framework to quickly detect fire type and analyse spatio-temporal distribution online. First, National Polar-orbiting Partnership (NPP) Visible Infrared Imaging Radiometer (VIIRS) Active Fire (ACF), Landsat 8 fire product, Moderate Resolution Imaging Spectroradiometer (MODIS) fire product (MCD14ML), and NPP-VIIRS Nighttime Fire Data (VNF) were obtained and stored into database. Then, industrial and biological fire were distinguished by taking full advantage of the industrial heat source dataset and land use dataset. Finally, quickly analysis can be realized online directly. The result shows that that system can carry out long time series analysis of multi-source fire data on different regions directly.

The cement industry, as one of the primary contributors to global greenhouse gas emissions, accounts for 7% of the world’s carbon dioxide emissions. There is an urgent need to establish a rapid method for detecting cement plants to facilitate effective monitoring. In this study, a comprehensive method based on YOLOv5-IEG and the Thermal Signature Detection module using Google Earth optical imagery and SDGSAT-1 thermal infrared imagery was proposed to detect large-scale cement plant information, including geographic location and operational status. The improved algorithm demonstrated an increase of 4.8% in accuracy and a 7.7% improvement in MAP@.5:95. In a specific empirical investigation in China, we successfully detected 781 large-scale cement plants with an accuracy of 90.8%. Specifically, of the 55 cement plants in Shandong Province, we identified 46 as operational and nine as non-operational. The successful application of advanced models and remote sensing technology in efficiently and accurately tracking the operational status of cement plants provides crucial support for environmental protection and sustainable development.

Accurate detection of industrial heat sources holds paramount importance in efforts towards reducing carbon emissions and safeguarding the environment. Traditional approaches of identifying these sources primarily rely on field research and statistical registration of pertinent industrial establishments. However, the traditional approaches are time-consuming, inefficient, having a small monitoring range and difficult to update in real time. Leveraging the rapidity and precision inherent in YOLO's detection capabilities, coupled with the accessibility of optical remote sensing imagery, this study proposes a tailored YOLO network for swift industrial heat source detection. YOLO's unique approach to treating object detection as a regression problem significantly enhances target recognition speed. Experimental findings affirm the efficiency of the proposed methodology in effectively identifying multi-scale industrial heat sources in remote sensing imagery. Moreover, the modified model showcases a notable improvement of 5.1% in precision rate and 11.7% in recall rate compared to the baseline.

Industrial heat sources (IHSs) are key contributors to anthropogenic heat, air pollution, and carbon emissions. Accurately and automatically detecting their production areas (IHSPAs) on a large scale is vital for environmental monitoring and decision making, yet this is challenged by the lack of high-resolution thermal data. Sustainable Development Science Satellite 1 (SDGSAT-1) thermal infrared spectrometer (TIS) data with the highest resolution (30 m) in the civilian field and a three-band advantage were first introduced to detect IHSPAs. In this study, an IHSPA identification model using multi-features extracted from SDGSAT-1 TIS and Landsat OLI data and support vector machine (SVM) was proposed. First, three brightness temperatures and four thermal radiation indices using SDGSAT-1 TIS and Landsat OLI data were designed to enlarge the temperature difference between IHSPAs and the background. Then, 10 features combined with three indices from Landsat OLI images with the same spatial resolution (30 m) and stable data were extracted. Second, an IHSPA identification model based on SVM and multi-feature extraction was constructed to identify IHSPAs. Finally, the IHS objects were manually delineated and verified using the identified IHSPAs and Google Earth images. Some conclusions were obtained from different comparisons in Wuhai, China: (1) IHSPA identification based on SVM using thermal and optical features can detect IHSPAs and obtain the best results compared with different features and identification models. (2) The importance of using thermal features from the SDGSAT-1 TIS to detect IHSPAs was demonstrated by different importance analysis methods. (3) Our proposed method can detect more IHSs, with greater spatial coverage and smaller areas, compared with the methods of Ma and Liu. This new way to detect IHSPAs can obtain higher-spatial-resolution emissions of IHSs on a large scale and help decision makers target environmental monitoring, management, and decision making in industrial plant processing.

It is crucial to detect and classify industrial heat sources for sustainable industrial development. Sustainable Development Science Satellite 1 (SDGSAT-1) thermal infrared spectrometer (TIS) data were first introduced for detecting industrial heat source production areas to address the difficulty in identifying factories with low combustion temperatures and small scales. In this study, a new industrial heat source identification and classification model using SDGSAT-1 TIS and Landsat 8/9 Operational Land Imager (OLI) data was proposed to improve the accuracy and granularity of industrial heat source recognition. First, multiple features (thermal and optical features) were extracted using SDGSAT-1 TIS and Landsat 8/9 OLI data. Second, an industrial heat source identification model based on a support vector machine (SVM) and multiple features was constructed. Then, industrial heat sources were generated and verified based on the topological correlation between the identification results of the production areas and Google Earth images. Finally, the industrial heat sources were classified into six categories based on point-of-interest (POI) data. The new model was applied to the Beijing–Tianjin–Hebei (BTH) region of China. The results showed the following: (1) Multiple features enhance the differentiation and identification accuracy between industrial heat source production areas and the background. (2) Compared to active-fire-point (ACF) data (375 m) and Landsat 8/9 thermal infrared sensor (TIRS) data (100 m), nighttime SDGSAT-1 TIS data (30 m) facilitate the more accurate detection of industrial heat source production areas. (3) Greater than 2~6 times more industrial heat sources were detected in the BTH region using our model than were reported by Ma and Liu. Some industrial heat sources with low heat emissions and small areas (53 thermal power plants) were detected for the first time using TIS data. (4) The production areas of cement plants exhibited the highest brightness temperatures, reaching 301.78 K, while thermal power plants exhibited the lowest brightness temperatures, averaging 277.31 K. The production areas and operational statuses of factories could be more accurately identified and monitored with the proposed approach than with previous methods. A new way to estimate the thermal and air pollution emissions of industrial enterprises is presented.

AbstractThe distribution of industrial heat sources (IHSs) is a crucial indicator for evaluating energy consumption and air pollution levels. However, there is a notable lack of IHS datasets in China that are frequently updated, span long periods, contain detailed characteristic information, have been individually validated and are publicly available. In this study, IHS datasets from China between 2012 and 2021 were constructed using the Visible Infrared Imaging Radiometer Suite (VIIRS) I Band 375 m NRT Active Fire/Hotspots (ACF) Product (VNP14IMGTDL_NRT) to monitor and analyse large‐scale IHSs. First, a density segmentation method based on an improved K‐means algorithm using ACF data and spatial topological correlation analysis was conducted to construct the IHS. Then, 4410 records covering China between 2012 and 2021, with 21 attributes, were obtained and verified, with an individual identification precision of 95.08% via manual verification based on high‐resolution remote‐sensing images and point of interest (POI) data. Finally, the trend of the spatiotemporal variation in IHSs was analysed using a long time series. The results showed that the spatial distribution of IHSs in China from 2012 to 2021 exhibited local aggregation and a gradual shift from east to west. In addition, the number of IHSs in China showed an initial increasing trend from 2012 to 2014, followed by a decrease since 2014, consistent with national energy reform‐related policies. The results of this study indicate the temporal variation in IHSs, enhance the precision of identifying fire location categories and demonstrate the potential for improving energy efficiency, reducing emissions and ensuring sustainable development in China.

The spatiotemporal distribution of industrial heat sources (IHS) is an important indicator for assessing levels of energy consumption and air pollution. Continuous, comprehensive, dynamic monitoring and publicly available datasets of global IHS (GIHS) are lacking and urgently needed. In this study, we built the first long-term (2012-2021) GIHS dataset based on the density-based spatiotemporal clustering method using multi-sources remote sensing data. A total of 25,544 IHS objects with 19 characteristics are identified and validated individually using high-resolution remote sensing images and point of interest (POI) data. The results show that the user's accuracy of the GIHS dataset ranges from 90.95% to 93.46%, surpassing other global IHS products in terms of accuracy, omission rates, and granularity. This long-term GIHS dataset serves as a valuable resource for understanding global environmental changes and making informed policy decisions. Its availability contributes to filling the gap in GIHS data and enhances our knowledge of global-scale industrial heat sources.

The prevalent high-energy, high-pollution and high-emission economic model has led to significant air pollution challenges in recent years. The industrial sector in the Beijing–Tianjin–Hebei (BTH) region is a notable source of atmospheric pollutants, with industrial heat sources (IHSs) being primary contributors to this pollution. Effectively managing emissions from these sources is pivotal for achieving air pollution control goals in the region. A new three-stage model using multi-source long-term data was proposed to estimate atmospheric, delicate particulate matter (PM2.5) concentrations caused by IHS. In the first stage, a region-growing algorithm was used to identify the IHS radiation areas. In the second and third stages, based on a seasonal trend decomposition procedure based on Loess (STL), multiple linear regression, and U-convLSTM models, IHS-related PM2.5 concentrations caused by meteorological and anthropogenic conditions were removed using long-term data from 2012 to 2021. Finally, this study analyzed the spatial and temporal variations in IHS-related PM2.5 concentrations in the BTH region. The findings reveal that PM2.5 concentrations in IHS radiation areas were higher than in background areas, with approximately 33.16% attributable to IHS activities. A decreasing trend in IHS-related PM2.5 concentrations was observed. Seasonal and spatial analyses indicated higher concentrations in the industrially dense southern region, particularly during autumn and winter. Moreover, a case study in Handan’s She County demonstrated dynamic fluctuations in IHS-related PM2.5 concentrations, with notable reductions during periods of industrial inactivity. Our results aligned closely with previous studies and actual IHS operations, showing strong positive correlations with related industrial indices. This study’s outcomes are theoretically and practically significant for understanding and addressing the regional air quality caused by IHSs, contributing positively to regional environmental quality improvement and sustainable industrial development.