Extraction and classification of built-up areas in three major urban agglomerations in China based on SDGSAT-1 glimmer imagery and dynamic threshold POI composite index

Urban agglomerations are critical for regional development, but extracting their built-up areas accurately is challenged by traditional night-time light data's limitations – low resolution (km to hectometre scale), over-saturation in urban cores, and missed detection in low-luminance areas. These issues hinder precise urban analysis, making improved extraction methods vital for urban planning and geographical research. This study focuses on extracting and classifying built-up areas in China's three major urban agglomerations. To address the above challenges, we use 10 m-resolution glimmer data from SDGSAT-1 and POI data to construct a dynamically adjustable weighted NTL-POI index. Combined with OTSU threshold segmentation and weighted K-means clustering, we realize hierarchical extraction of built-up areas. Results show that integrating SDGSAT-1 glimmer imagery and POI data precisely depicts urban internal structures, effectively detects low-luminance built-up areas, and optimizes accuracy and shape indicators. F1 scores for the Beijing-Tianjin-Hebei, Yangtze River Delta, and Guangdong-Hong Kong-Macao Greater Bay Area reach 0.74, 0.78, and 0.87, respectively. This provides accurate data support for urban research and holds value in urban planning and geographical analysis.