An improved height sampling approach used for global urban building height mapping

• Mapping global urban building height for the year of 2020. • Propose an improved height sampling method combining ALOS AW3D30 and GEDI. • The RMSE of estimation results reached 4.71 m, 6.36 m, and 10.07 m in Europe, the United States, and China, respectively. Building height serves as fundamental information for characterizing urban landscapes and morphology, as influencing various aspects of the urban environment. While traditional methods of obtaining building height are often limited by spatial coverage and proprietary constraints, remote sensing data provides an alternative for indirect estimation. Several height products developed across different spatial scales, yet challenges remain due to the spatial and temporal incompleteness of publicly available building height samples, which introduce inherent uncertainties in global height mapping. This study proposed an improved approach for building height sampling that combines the ALOS AW3D30 and Global Ecosystem Dynamics Investigation (GEDI) data. Both datasets are open-access, providing a more comprehensive and representative sample base for model construction. To address temporal discrepancies between these two data, continuous change detection and classification (CCDC) algorithm was employed to remove invalid height samples. Subsequently, we trained random forest (RF) models using a combination of multi-source remote sensing data, including radar data, optical data, nighttime light data, terrain data, and footprint data, to generate a global urban building height map for the year of 2020. Reference samples from Europe, the United States, and China were employed to validate the model, indicating a high degree of consistency between the references and estimated results (R 2 = 0.55–0.75, RMSE = 4.71–10.07 m). Moreover, our findings indicated that over 20 % of regions globally experienced rapid urbanization, with average building heights exceeding 10 m, particularly in southern China. The approach proposed in this study provides effective support for building height estimation, particularly in address the limitations of lack of incomplete and representative height samples in global mapping.