Abstract China’s New-Type Urbanization Plan since 2015, the world’s largest urbanization endeavor, reshapes the nation’s socio-economic landscape but lacks high-precision, fine-scale progress monitoring. Urban construction sites (UCS), barometers of urban spatial expansion and renewal, offer a detailed observational window. A sub-meter resolution deep-learning framework for nationwide UCS mapping is proposed. Using a Segment Anything Model (SAM)-enabled weakly supervised method for pixel-level UCS annotation, a spectral–texture dual-branch segmentation network (STTBNet) with 94.3% overall accuracy identifies 541 177 UCS (including 10 m² micro-sites) across 372 cities. K-means clustering partitions cities into four typologies, uncovering a dual-track parallel pattern (incremental expansion + stock optimization) vs the classical ‘growth–decline–renewal’ trajectory. Spatial analysis shows UCS construction correlates with annual PM₂.₅ concentrations; green dust-proof net coverage (10%) fails to curb pollution. The framework serves as a ‘microscope’ for evaluating new-type urbanization and supports sustainable planning.
Li et al. (Tue,) studied this question.