Tailings ponds’ failure and environmental pollution make tailings monitoring very important. Remote sensing technology can quickly and widely obtain ground information and has become one of the important means of tailings monitoring. However, the efficiency and accuracy of traditional remote sensing monitoring technology have difficulty meeting the management needs. At the same time, affected by factors such as the geographical environment and imaging conditions, tailings have various manifestations in remote sensing images, which all bring challenges to the accurate acquisition of tailings information in large areas. By improving You Only Look Once (YOLO) v5s, this study designs a deep learning-based framework for the large-scale extraction of tailings ponds information from the entire high-resolution remote sensing images. For the improved YOLOv5s, the Swin Transformer is integrated to build the Swin-T backbone, the Fusion Block of efficient Reparameterized Generalized Feature Pyramid Network (RepGFPN) in DAMO-YOLO is introduced to form the RepGFPN Neck, and the head is replaced with Decoupled Head. In addition, sample boosting strategy (SBS) and global non-maximum suppression (GNMS) are designed to improve the sample quality and suppress repeated detection frames in the entire image, respectively. The model test results based on entire Gaofen-6 (GF-6) high-resolution remote sensing images show that the F1 score of tailings ponds is significantly improved by 12.22% compared with YOLOv5, reaching 81.90%. On the basis of both employing SBS, the improved YOLOv5s boots the mAP@0.5 of YOLOv5s by 5.95%, reaching 92.15%. This study provides a solution for tailings ponds’ monitoring and ecological environment management.

Urbanization is typically accompanied by the relocation and reconstruction of industrial areas due to limited space and environmental requirements, particularly in the case of a capital city. Shougang Group, one of the largest steel mill operators in China, was relocated from Beijing to Hebei Province. To study the thermal environmental changes at the Shougang industrial site before and after relocation, four Landsat images (from 2000, 2005, 2010 and 2016) were used to calculate the land surface temperature (LST). Using the urban heat island ratio index (URI), we compared the LST values for the four images of the investigated area. Following the relocation of Shougang Group, the URI values decreased from 0.55 in 2005 to 0.21 in 2016, indicating that the surface urban heat island effect in the area was greatly mitigated; we infer that this effect was related to steel production. This study shows that the use of Landsat images to assess industrial thermal pollution is feasible. Accurate and rapid extraction of thermal pollution data by remote sensing offers great potential for the management of industrial pollution sources and distribution, and for technical support in urban planning departments.

The identification of urban functional zones (UFZs) is crucial for urban planning and optimizing industrial layout. Fusing remote sensing images and social perception data is an effective way to identify UFZs. Previous studies on UFZs recognition often ignored band information outside the red–green–blue (RGB), especially three-dimensional (3D) urban morphology information. In addition, the probabilistic methods ignore the potential semantic information of Point of Interest (POI) data. Therefore, we propose an “Image + Text” multimodal data fusion framework for UFZs recognition. To effectively utilize the information of Gaofen-7(GF-7) stereo images, we designed a semi-transfer UFZs recognition model. The transferred model uses the pre-trained model to extract the deep features from RGB images, and a small self-built convolutional network is designed to extract the features from RGB bands, near-infrared (NIR) band, and normalized digital surface model (nDSM) generated by GF-7. Latent Dirichlet allocation (LDA) is employed to extract POI semantic features. The fusion features of the deep features of the GF-7 image and the semantic features of POI are fed into a classifier to identify UFZs. The experimental results show that: (1) The highest overall accuracy of 88.17% and the highest kappa coefficient of 83.91% are obtained in the Beijing Fourth Ring District. (2) nDSM and NIR data improve the overall accuracy of UFZs identification. (3) POI data significantly enhance the recognition accuracy of UFZs, except for shantytowns. This UFZs identification is simple and easy to implement, which can provide a reference for related research. However, considering the availability of POI data distribution, other data with socioeconomic attributes should be considered, and other multimodal fusion strategies are worth exploring in the future.

Automatically extracting urban buildings from remote sensing images has essential application value, such as urban planning and management. Gaofen-7 (GF-7) provides multi-perspective and multispectral satellite images, which can obtain three-dimensional spatial information. Previous studies on building extraction often ignored information outside the red–green–blue (RGB) bands. To utilize the multi-dimensional spatial information of GF-7, we propose a dual-stream multi-scale network (DMU-Net) for urban building extraction. DMU-Net is based on U-Net, and the encoder is designed as the dual-stream CNN structure, which inputs RGB images, near-infrared (NIR), and normalized digital surface model (nDSM) fusion images, respectively. In addition, the improved FPN (IFPN) structure is integrated into the decoder. It enables DMU-Net to fuse different band features and multi-scale features of images effectively. This new method is tested with the study area within the Fourth Ring Road in Beijing, and the conclusions are as follows: (1) Our network achieves an overall accuracy (OA) of 96.16% and an intersection-over-union (IoU) of 84.49% for the GF-7 self-annotated building dataset, outperforms other state-of-the-art (SOTA) models. (2) Three-dimensional information significantly improved the accuracy of building extraction. Compared with RGB and RGB + NIR, the IoU increased by 7.61% and 3.19% after using nDSM data, respectively. (3) DMU-Net is superior to SMU-Net, DU-Net, and IEU-Net. The IoU is improved by 0.74%, 0.55%, and 1.65%, respectively, indicating the superiority of the dual-stream CNN structure and the IFPN structure.

Built-up areas extraction from satellite images is an important aspect of urban planning and land use; however, this remains a challenging task when using optical satellite images. Existing methods may be limited because of the complex background. In this paper, an improved boosting learning saliency method for built-up area extraction from Sentinel-2 images is proposed. First, the optimal band combination for extracting such areas from Sentinel-2 data is determined; then, a coarse saliency map is generated, based on multiple cues and the geodesic weighted Bayesian (GWB) model, that provides training samples for a strong model; a refined saliency map is subsequently obtained using the strong model. Furthermore, cuboid cellular automata (CCA) is used to integrate multiscale saliency maps for improving the refined saliency map. Then, coarse and refined saliency maps are synthesized to create a final saliency map. Finally, the fractional-order Darwinian particle swarm optimization algorithm (FODPSO) is employed to extract the built-up areas from the final saliency result. Cities in five different types of ecosystems in China (desert, coastal, riverside, valley, and plain) are used to evaluate the proposed method. Analyses of results and comparative analyses with other methods suggest that the proposed method is robust, with good accuracy.

Accurately assessing seismic hazard-prone environments in complex tectonic regions faces multiple challenges, particularly in distinguishing physical drivers from environmental proxies and mitigating spatial overfitting due to intricate interactions between geological and environmental factors. Taking Myanmar as a case study, this research proposes an integrated framework fusing multi-source geospatial data with interpretable ensemble learning to map catalog-conditioned seismic hazard-prone environments based on historical data (1970–2024). Unlike traditional perspectives that treat surface factors as direct causal drivers, we reinterpret variables such as elevation and slope as geomorphic phenotypic signatures that co-vary with seismic activity. To eliminate data leakage caused by spatial autocorrelation, a rigorous Spatial Block Cross-Validation (SBCV) strategy was implemented. Results indicate that compared to the overly optimistic outcome yielded by random splitting (AUC = 0.948), SBCV provides a more robust and conservative performance assessment (AUC = 0.890), effectively confirming the model’s generalization capability across unseen geographic units. Among the five compared models, the Gradient Boosting Decision Tree (GBDT) demonstrated superior performance. To resolve the “black-box” nature of machine learning, we integrated micro-scale SHAP values with macro-scale GeoDetector analysis. Both validation methods consistently identified Elevation, Slope, and Distance to Active Faults as core discriminators (cumulative contribution > 60%), revealing that high-susceptibility zones are primarily characterized by high-relief terrain signatures that spatially coincide with active tectonic belts. Furthermore, by overlaying Nighttime Light (NTL) data, the study identified a significant risk overlap of “high-hazard environments and high exposure” within the Mandalay-Sagaing urban corridor. This study provides a scientifically rigorous and spatially validated paradigm for seismic environmental pattern recognition in data-scarce regions, offering an actionable scientific basis for regional disaster risk governance.