ESSDD Earth System Science Data Discussions ESSDD Earth Syst. Sci. Data Discuss. 1866-3591 Göttingen, Germany 10.5194/essd-2026-381 A global urban built-up area dataset for cities with populations exceeding 300,000 (2000–2025) Gunasekera Dinoo https://orcid.org/0000-0001-7007-6957 1 2 3 Sun Zhongchang 1 2 3 Du Wenjie https://orcid.org/0000-0002-2253-3049 1 2 3 Gao Jian 4 Zhang Yunpeng 1 2 3 Zhou Yuyu 5 Ndugwa Robert 6 Guo Huadong 1 2 3 University of Chinese Academy of Sciences, 100094, Beijing, China Aerospace Information Research Institute, Chinese Academy of Sciences, 100094, Beijing, China International Research Center of Big Data for Sustainable Development Goals, 100094, Beijing, China School of Internet of Things, Nanjing University of Posts and Telecommunications, Nanjing 210023, China Department of Geography, The University of Hong Kong, 999077, Hong Kong, China UN-Habitat, Nairobi, Kenya 16 09 2026 2026 1 25 Copyright: © 2026 Dinoo Gunasekera et al. 2026 This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/ This article is available from https://essd.copernicus.org/preprints/essd-2026-381/ The full text article is available as a PDF file from https://essd.copernicus.org/preprints/essd-2026-381/essd-2026-381.pdf

Urban land-use efficiency (LUE), defined under Sustainable Development Goal (SDG) 11.3.1, is widely used to evaluate the coordination between urban expansion and population growth. However, its global assessment remains constrained by inconsistent definitions of urban boundaries and the lack of long-term, 30 m spatial resolution urban built-up area (UBA) datasets. Here we present the Global Urban Built-up Area Dataset (GUBAD), a multi-epoch dataset at 30 m spatial resolution covering 1,611 cities worldwide with populations exceeding 300,000 inhabitants from 2000 to 2025. GUBAD applied standardized UN-defined urban agglomeration boundaries using a spatial morphological framework integrating impervious surface data with population constraints. Impervious surface areas (ISA) were extracted using Random Forest classification of Landsat and Sentinel-1/2 imagery, combined with isotonic regression-based temporal correction. Validation results yielded a mean overall accuracy of 95.18 % and Kappa coefficient of 0.90 across all epochs. GUBAD shows strong agreement with GHSL-SMOD, DEGURBA, and UN-Habitat products while providing enhanced spatial detail. Unlike global urban boundary datasets that delineate administrative-functional city extents, GUBAD explicitly targets the physically built-up fabric derived from impervious surfaces under population-density and contiguity constraints. We further demonstrate the utility of GUBAD for estimating SDG indicator 11.3.1, revealing spatiotemporal variations in the relationship between land consumption rate and population growth rate (LCRPGR) at the global city scale. GUBAD provides a temporally consistent, city-scale UBA dataset based on standardized UN urban agglomeration definitions, enabling reproducible monitoring of urban expansion and SDG 11.3.1 assessment globally. GUBAD is available at Zenodo under CC BY 4.0 (Gunasekera et al., 2025) with DOI: <a href="https://doi.org/10.5281/zenodo.20051123" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.20051123</a>. The dataset is released as version 1.0 with planned updates every 5 years.

 National Natural Science Foundation of China 42361144884 42171291 42471363 Agency for Science, Technology and Research 313GJHZ2025038MI