A Study on Urban Built-Up Area Extraction Methods and Consistency Evaluation Based on Multi-Source Nighttime Light Remote Sensing Data: A Case Study of Wuhan City

Accurate delineation of urban built-up areas is critical for urban monitoring and planning. We evaluated the performance and consistency of three widely used methods—thresholding, multi-temporal image fusion, and support vector machine (SVM)—across three major nighttime light (NTL) datasets (DMSP/OLS, SNPP/VIIRS, and Luojia-1). We developed a unified methodological framework and applied it to Wuhan, China, encompassing data preprocessing, feature construction, classification, and cross-dataset validation. The results show that SNPP/VIIRS combined with thresholding or SVM achieved highest accuracy (kappa coefficient = 0.70 and 0.61, respectively) and spatial consistency (intersection over union, IoU = 0.76), attributable to its high radiometric sensitivity and temporal stability. DMSP/OLS exhibited robust performance with SVM (kappa = 0.73), likely benefiting from its long historical coverage, while Luojia-1 was constrained by limited temporal availability, hindering its suitability for temporal fusion methods. This study highlights the critical influence of sensor characteristics and method–dataset compatibility on extraction outcomes. While traditional methods provide interpretability and computational efficiency, the findings suggest a need for integrating deep learning models and hybrid strategies in future work. These advancements could further improve accuracy, robustness, and transferability across diverse urban contexts.