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.

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.

To achieve the objective of a “15 min living circle” for educational services, this study develops an integrated method for primary school site selection in Tianjin, China, by combining multi-source data and ensemble learning techniques. At a 500 m grid scale, a suitability prediction model was constructed based on the existing distribution of primary schools, utilizing Random Forest (RF) and Extreme Gradient Boosting (XGBoost) models. Comprehensive evaluation, feature importance analysis, and SHAP (SHapley Additive exPlanations) interpretation were conducted to ensure model reliability and interpretability. Spatial overlay analysis, incorporating population structure and the education supply–demand ratio, identified highly suitable areas for primary school construction. The results demonstrate: (1) RF and XGBoost achieved evaluation metrics exceeding 85%, outperforming traditional single models such as Logistic Regression, SVM, KNN, and CART. Validation against actual primary school distributions yielded accuracies of 84.70% and 92.41% for RF and XGBoost, respectively. (2) SHAP analysis identified population density, proximity to other educational institutions, and accessibility to transportation facilities as the most critical factors influencing site suitability. (3) Suitable areas for primary school construction are concentrated in central Tianjin and surrounding areas, including Baoping Street (Baodi District), Huaming Street (Dongli District), and Zhongbei Town (Xiqing District), among others, to meet high-quality educational service demands.

Cyanobacterial blooms pose a serious threat to freshwater ecosystems, necessitating accurate remote sensing monitoring. Although red-edge bands show potential in terrestrial monitoring, their multi-dimensional features (i.e., spectral, textural, and index-based characteristics) remain underutilized for aquatic blooms. This study leverages the dual red-edge bands (710 nm and 750 nm) of GF-6/WFV to enhance cyanobacterial bloom identification in Lake Taihu. Multi-temporal images from 2019–2023 were used to construct red-edge features in three dimensions: spectral (evaluated via adaptive band selection method) and Jeffries–Matusita–Bhattacharyya distance), texture (based on Gray Level Co-occurrence Matrix and principal component analysis), and indices (nine vegetation indices ranked by Random Forest importance). Twelve feature-combination schemes were designed and implemented with a Random Forest classifier. Results show that red-edge features consistently improve identification accuracy. Quantitatively, compared to the basic four-band (RGBN) combination, the 710 nm band improved spectral separability by an average of 9.63%, whereas the 750 nm band yielded a lower average improvement of 5.69%. Red-edge indices, especially the modified chlorophyll absorption reflectance index 1 (MCARI1) and normalized difference red-edge index (NDRE), exhibited higher importance than non-red-edge indices. All schemes incorporating red-edge features achieved mean overall accuracies of 92.8–94.9% and Kappa coefficients of 0.86–0.94, surpassing the basic four-band scheme. Among these features, red-edge indices contributed most significantly to accuracy gains, increasing the overall accuracy by an average of 0.36–6.06% and the Kappa coefficient by up to 0.06. The enhancement effect of the red-edge 710 nm band features was superior to that of the 750 nm band. This study demonstrates that multi-dimensional red-edge features effectively enhance the identification accuracy of cyanobacterial blooms and provides a methodological reference for operational GF-6 applications in water quality monitoring.