Deep-learning-based sub-meter urban construction-site mapping reveals China's dual-track urban renewal.

China's New-Type Urbanization Plan since 2015-the world's largest urbanization endeavor-reshapes the nation's socioeconomic landscape but lacks high-precision, fine-scale progress monitoring. Urban construction sites (UCSs)-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-enabled weakly supervised method for pixel-level UCS annotation, a spectral-texture dual-branch segmentation network with 94.3% overall accuracy identifies 541 177 UCSs (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 that 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.