Urban vacant land identification and its distribution rules of China's urban system based on fast segment anything model

Urban Vacant Land (UVL) is both a resource and a challenge for sustainable urban development. However, large-scale UVL identification remains understudied. This study addresses this gap by proposing an innovative automated UVL identification method utilizing the cutting-edge Segment Anything Model (SAM), applied across all 2446 Natural Cities (NCs) in China. Our findings reveal several distribution patterns. First, the UVL ratio, which refers to the proportion of the UVL area in each NC, follows a log-normal distribution and remains independent of city size. Second, the UVL area adheres to Zipf's law and scaling law, where larger cities tend to have larger UVL areas. Third, we identify five distinct UVL spatial types based on intra-city distribution patterns. In large cities, UVL tends to cluster to form local types, while in smaller cities, they are more dispersed, forming central, peripheral, and scatter types. Forth, regional analysis reveals significant spatial heterogeneity in UVL types across China. Global and peripheral types require special attention, as they present unique challenges due to high UVL ratios and larger average UVL sizes. This study not only advances the methodological framework for UVL identification and providing a comprehensive UVL dataset for China, but also delivers actionable insights for sustainable urban development through the application of AI technology.