Local Climate Zone (LCZ) classification is essential for urban microclimate modeling and heat mitigation planning. Traditional methods relying on manual sampling face limitations in scalability, objectivity, and handling spatial heterogeneity. This study presents an automated framework for LCZ sample generation, facilitating efficient large-scale LCZ mapping and LCZ-based urban climate analysis and geospatial applications. To this aim, it proposes a dual-path automated framework integrating GIS-driven sample generation to enhance LCZ classification accuracy: a multi-parameter Synergistic Optimization approach for urban LCZs and a Distance-driven Maximum Coverage method for natural LCZs. Specifically, urban samples are selected via multi-objective optimization and Pareto front screening for quality and representativeness, while the selection of natural samples prioritizes spatial coverage and diversity. Combining urban morphological parameters with Sentinel-2 imagery and a Random Forest classifier yielded a final accuracy of 0.95 in our test site, confirming the framework’s effectiveness.

Accurate Local Climate Zone (LCZ) classification is essential for urban climate studies, environmental monitoring, and sustainable city planning. Recent advances in Deep Learning (DL) have significantly improved LCZ mapping, but challenges remain in capturing spatial position features and distinguishing spectrally similar land cover types. This letter proposes multi-scale Coordinate Attention-based Multi-Stream Local Climate Zone Network (MSCA-MSLCZNet), a multi-stream DL framework integrating multi-scale feature processing, attention mechanisms, and rule-based refinement to enhance LCZ classification performance. The model is evaluated on the So2Sat LCZ42 dataset, outperforming baseline methods in overall accuracy (OA), OA of built-up classes (OA_bu), OA of natural classes (OA_n), and Kappa. Further validation on Milan LCZ mapping confirms its generalization capability, demonstrating strong classification performance in built-up areas and improved urban structure delineation. Comparative experiments highlight the model’s ability to better differentiate urban structures and built-up zones from natural landscapes. MSCA-MSLCZNet proves effective for large-scale LCZ mapping, offering improved classification accuracy and adaptability to diverse geographic regions.

Local Climate Zone (LCZ) classification provides a standardized framework for characterizing urban morphology and its climatic implications. However, most existing remote sensing-based LCZ mapping methods rely on pixel-level classification and multispectral data alone, which limits their ability to capture urban scene heterogeneity and to distinguish structurally similar LCZ classes. In this paper, we propose LCZ-HMSSNet, a deep learning framework for scene-level LCZ classification that integrates PRISMA hyperspectral images with Sentinel-2 multispectral data. The proposed approach exploits both the spectral richness of hyperspectral data and the spatial context provided by multispectral observations, and incorporates a spatial–spectral feature separation mechanism to enhance the discriminability of the fused representations. Experiments conducted across six representative European cities evaluate the proposed method from multiple perspectives, including comparisons with different classification models, data contribution analysis, and structural ablation studies. The results demonstrate that the proposed method consistently outperforms MSI-only and existing LCZ classification approaches, achieving an overall accuracy (OA) of 0.988 and a Kappa of 0.985. In addition, the small-sample experiments indicate the robustness and potential of the proposed model, providing a practical reference for future LCZ mapping in data-scarce scenarios.

Impervious surface area (ISA) has been recognized as a significant indicator for evaluating levels of urbanization and the quality of urban ecological environments. ISA extraction methods based on supervised classification usually rely on a large number of manually labeled samples, the production of which is a time-consuming and labor-intensive task. Furthermore, in arid areas, man-made objects are easily confused with bare land due to similar spectral responses. To tackle these issues, a self-trained deep-forest (STDF)-based ISA extraction method is proposed which exploits the complementary information contained in multispectral and polarimetric synthetic aperture radar (PolSAR) images using limited numbers of samples. In detail, this method consists of three major steps. First, multi-features, including spectral, spatial and polarimetric features, are extracted from Sentinel-2 multispectral and Chinese GaoFen-3 (GF-3) PolSAR images; secondly, a deep forest (DF) model is trained in a self-training manner using a limited number of samples for ISA extraction; finally, ISAs (in this case, in three major cities located in Central Asia) are extracted and comparatively evaluated. The experimental results from the study areas of Bishkek, Tashkent and Nursultan demonstrate the effectiveness of the proposed method, with an overall accuracy (OA) above 95% and a Kappa coefficient above 0.90.