GLObal Building heights for Urban Studies (UT-GLOBUS) for city- and street- scale urban simulations: Development and first applications
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Abstract (arXiv)
We introduce University of Texas - Global Building heights for Urban Studies (UT-GLOBUS), a dataset providing building heights and urban canopy parameters (UCPs) for more than 1200 cities or locales worldwide. UT-GLOBUS combines open-source spaceborne altimetry (ICESat-2 and GEDI) and coarse-resolution urban canopy elevation data with a machine-learning model to estimate building-level information. Validation using LiDAR data from six US cities showed UT-GLOBUS-derived building heights had a root mean squared error (RMSE) of 9.1 meters. Validation of mean building heights within 1-km^2 grid cells, including data from Hamburg and Sydney, resulted in an RMSE of 7.8 meters. Testing the UCPs in the urban Weather Research and Forecasting (WRF-Urban) model resulted in a significant improvement (55% in RMSE) in intra-urban air temperature representation compared to the existing table-based local climate zone approach in Houston, TX. Additionally, we demonstrated the dataset's utility for simulating heat mitigation strategies and building energy consumption using WRF-Urban, with test cases in Chicago, IL, and Austin, TX. Street-scale mean radiant temperature simulations using the Solar and LongWave Environmental Irradiance Geometry (SOLWEIG) model, incorporating UT-GLOBUS and LiDAR-derived building heights, confirmed the dataset's effectiveness in modeling human thermal comfort in Baltimore, MD (daytime RMSE = 2.85 C). Thus, UT-GLOBUS can be used for modeling urban hazards with significant socioeconomic and biometeorological risks, enabling finer scale urban climate simulations and overcoming previous limitations due to the lack of building information.
Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 1 of its 42 references that are in the map. First posted on arXiv 24 May 2022.
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Citing papers per year: 2023: 1; 2024: 0; 2025: 1; 2026: 3.
5 papers.
- 2023GlobalMapper: Arbitrary-Shaped Urban Layout Generationcited by 15
- 2025DeepShade: Enable Shade Simulation by Text-conditioned Image Generationcited by 4
- 2026GeoFormer: A Lightweight Swin Transformer for Joint Building Height and Footprint Estimation from Sentinel Imagerycited by 0
- 2026Morphology-Guided Cross-Task Coupling for Joint Building Height and Footprint Estimationcited by 0
- 2026ShadeBench: A Benchmark Dataset for Building Shade Simulation in Sustainable Societycited by 0
- 2026ShadeBench: A Benchmark Dataset for Building Shade Simulation in Sustainable Society19 May 2026
- 2026Morphology-Guided Cross-Task Coupling for Joint Building Height and Footprint Estimation6 May 2026
- 2026GeoFormer: A Lightweight Swin Transformer for Joint Building Height and Footprint Estimation from Sentinel Imagery10 Feb 2026
- 2025DeepShade: Enable Shade Simulation by Text-conditioned Image Generation16 Jul 2025
- 2023GlobalMapper: Arbitrary-Shaped Urban Layout Generation19 Jul 2023