Mapping three decades of urban growth in China: a 30 m annual building height dataset (1990–2019)

Abstract. Long-term building height data are critical for analyzing urban morphological evolution and renewal processes, yet such datasets at fine spatial resolutions remain scarce for large geographical regions. This study proposes a framework to generate continuous annual building height maps for China at 30 m spatial resolution from 1990 to 2019, integrating multi-source remote sensing data (Landsat, Sentinel-1/2, etc.) through the eXtreme Gradient Boosting (XGBoost) model. The framework reconstructs Vertical-Vertical (VV) band, incorporates reference data derived from the Continuous Change Detection and Classification (CCDC) algorithm, and utilizes Total Variation (TV) denoising to achieve temporal consistency, while retaining inter-annual building height variations. Validation results demonstrate the stable performance of the building height maps over the past three decades, with nationwide RMSE values ranging between 3.20 and 4.27 m. Comparisons confirm that our results are consistent with reference height datasets and capture the temporal evolution of building heights driven by urban development and renewal. Furthermore, our dataset shows pronounced horizontal and vertical expansion of Chinese cities between 1990 and 2019, as the total impervious surface area increases from 54 407.03 to 170 595.06 km2 and overall building volume rises from 365.98 to 882.43 km3. Provincial contributions to national building volume change substantially over time, with Hebei (11.3 %), Shandong (10.6 %), and Heilongjiang (10.2 %) leading in 1990, while Shandong (9.8 %), Hebei (9.5 %) and Guangdong (9.4 %) are in the leading positions in 2019. The resulting annual 30 m resolution building height datasets, made openly accessible, provide a valuable foundation for cross-city comparisons, long-term three-dimensional (3D) urban morphology studies, and policy-relevant planning in fast-growing Chinese cities. The dataset is available at https://doi.org/10.6084/m9.figshare.29918978 (Zhang et al., 2025b).