GLObal Building heights for Urban Studies (UT-GLOBUS) for city- and street- scale urban simulations: Development and first applications

TL;DR

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.

Abstract

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.

Figures (10)

• Figure 1: (a) Comparison of building footprints derived using GBFE from 3-meter resolution satellite images with ground truth for Vienna, Austria, and (b) Illustration of OSM building footprints processed for Milan, Italy. GBFE: generative building feature estimation and OSM: OpenStreetMaps.
• Figure 2: UT-GLOBUS methodology for predicting and validating building heights. AGLH: Above ground-level height, ICESat-2: Ice, Cloud, and land Elevation Satellite-2, GEDI: Global Ecosystem Dynamics Investigation, ALOS: Advanced Land Observation Satellite, LiDAR: Light Detection and Ranging.
• Figure 3: Scatter plots (top row) showing the agreement of the random forest model prediction of individual building heights with ground truth for the validation and testing datasets. Box plots (bottom row) present the residues from prediction for $10$-meter height bins.
• Figure 4: Spatial comparison of UT-GLOBUS building heights at 1 km$^2$ resolution against LiDAR (ground truth) and Li et al., (2020) for Austin, Texas. The figure also shows the scatter plots comparing the LiDAR-derived building heights with UT-GLOBUS and Li et al., (2020) datasets.
• Figure 5: Spatial comparison of UT-GLOBUS plan area fraction ($\lambda_p$), averaged building heights ($h_a$) and building surface to plan area ratio ($\lambda_b$) at 1 km$^2$ spatial resolution against Spanish building inventory dataset (ground truth).