Sandy beaches are vital geomorphic units with ecological, social, and economic significance, playing a key role in coastal protection and ecosystem regulation. However, they are increasingly threatened by climate change and human activities, highlighting the need for large-scale, high-precision monitoring to support sustainable management. Existing remote-sensing-based sandy beach extraction methods face challenges such as suboptimal feature selection and reliance on single data sources, limiting their generalization and accuracy. This study proposes a novel sandy beach extraction framework that integrates multi-source data, feature optimization, and collaborative modeling, with Fujian Province, China, as the study area. The framework combines Sentinel-1/2 imagery, nighttime light data, and terrain data to construct a comprehensive feature set containing 44 spectrum, index, polarization, texture, and terrain variables. The optimal feature subsets are selected using the Recursive Feature Elimination (RFE) algorithm. Six machine learning models—Random Forest (RF), Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LGBM), Gradient Boosting Machine (GBM), Adaptive Boosting (AdaBoost), and Categorical Boosting (CatBoost)—along with an ensemble learning model, are employed for comparative analysis and performance optimization. The results indicate the following. (1) All models achieved the best performance when integrating all five types of features, with the average overall F1-score and accuracy reaching 0.9714 and 0.9733, respectively. (2) The number of optimal features selected by RFE varied by model, ranging from 19 to 36. The ten most important features across models were Band 2 (B2), Elevation, Band 3 (B3), VVVH_SUM, Spatial Average (SAVG), VH, Enhanced Water Index (EWI), Slope, Variance (VAR), and Normalized Difference Vegetation Index (NDVI). (3) The ensemble learning model outperformed all others, achieving an average overall accuracy, precision, recall, and F1-score of 0.9750, 0.9733, 0.9725, and 0.9734, respectively, under the optimal feature subset. A total of 555 sandy beaches were extracted in Fujian Province, covering an area of 43.60 km² with a total perimeter of 1263.59 km. This framework demonstrates strong adaptability and robustness in complex coastal environments, providing a scalable solution for intelligent sandy beach monitoring and refined resource management.

Fine-scale identification of urban building functions is essential for understanding urban spatial structure, socioeconomic organization, and sustainable urban development. However, large-scale building function mapping remains constrained by reliance on proprietary data, insufficient representation of geographical context, and limited cross-city generalization. To address these challenges, this study proposes a multi-source geographical knowledge fusion framework for fine-scale building function classification using exclusively open-source data. In addition to conventional morphological, POI-based, and spectral features, the framework systematically integrates open-source geographical environmental features to characterize accessibility, infrastructure relationships, ecological surroundings, and environmental conditions at the building level. Beijing is selected as the training area, while Tianjin is used for independent cross-city validation. Ten representative models, including deep learning, ensemble learning, and traditional machine learning methods, are systematically evaluated for classifying six building function types. Results show that the systematic integration of geographical environmental features improves classification performance and interpretability. Deep learning and ensemble models outperform traditional methods, with CNN achieving the highest accuracy of 87.07% in Beijing and 69.83% in Tianjin. Feature contribution analysis further indicates that geographical environmental features play a dominant role in functional discrimination, while POI features provide important socio-semantic information. Overall, this study provides a reproducible open-data framework for urban building function mapping, supporting sustainable urban planning, land-use optimization, infrastructure allocation, and smart city governance.