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, et al.) 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 stable performance of the building height estimates over the past three decades, with nationwide RMSE values ranging between 5.96 and 6.69 m. Comparisons with existing datasets confirm consistency with reference building heights and their temporal evolution 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 56,413.68 to 174,320.66 km² and overall building volume rises from 471.24 to 884.69 km³. Provincial contributions to national building volume change substantially over time, with Hebei (12.9 %), Shandong (11.4 %), and Henan (10.3 %) leading in 1990, while Shandong (10.0 %), Guangdong (8.0 %) and Jiangsu (8.0 %) 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.
Competing interests: At least one of the (co-)authors is a member of the editorial board of Earth System Science Data.
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