ArticleScientific data2023
Global 30 meters spatiotemporal 3D urban expansion dataset from 1990 to 2010.
Article in Scientific data, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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Who cites it
9 citing papers in PubMed.
- Global increases in built-up volume indicate more divergent and less dispersed urban expansion patterns.Nature communications · 2026Article
- A Multi-Modal, Multi-Temporal, Multi-Resolution Benchmark Dataset for Building Height Estimation.Scientific data · 2025Article
- FU3D: the first global projections of future urban three-dimensional (3D) expansion for the 21st century under shared socioeconomic pathways.Scientific data · 2025Article
- Narrowing the gap for city building height predictions.Scientific reports · 2025Article
- Vectorized building rooftop prints of the Qinghai-Tibetan Plateau and its neighboring regions.Scientific data · 2025Article
- A global product of 150-m urban building height based on spaceborne lidar.Scientific data · 2024Article
- Annual Impervious Surface Data from 2001-2020 for West African Countries: Ghana, Togo, Benin and Nigeria.Scientific data · 2024Article
- GLAMOUR: GLobAl building MOrphology dataset for URban hydroclimate modelling.Scientific data · 2024Article
- Global 30 meters spatiotemporal 3D urban expansion dataset from 1990 to 2010.Scientific data · 2023Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Understanding the spatiotemporal dynamics of global 3D urban expansion over time is becoming increasingly crucial for achieving long-term development goals. In this study, we generated a global dataset of annual urban 3D expansion (1990-2010) using World Settlement Footprint 2015 data, GAIA data, and ALOS AW3D30 data with a three-step technical framework: (1) extracting the global constructed land to generate the research area, (2) neighborhood analysis to calculate the original normalized DSM and slope height of each pixel in the study area, and (3) slope correction for areas with a slope greater than 10° to improve the accuracy of estimated building heights. The cross-validation results indicate that our dataset is reliable in the United States(R
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Registered trials
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