  Urban weather and climate studies continue to be important as extreme events
cause economic loss and impact public health. Weather models seek to represent
urban areas but are oversimplified due to data availability, especially
building information. This paper introduces a novel Level of Detail-1 (LoD-1)
building dataset derived from a Deep Neural Network (DNN) called GLObal
Building heights for Urban Studies (GLOBUS). GLOBUS uses open-source datasets
as predictors: Advanced Land Observation Satellite (ALOS) Digital Surface Model
(DSM) normalized using Shuttle Radar Topography Mission (SRTM) Digital
Elevation Model (DEM), Landscan population density, and building footprints.
The building information from GLOBUS can be ingested in Numerical Weather
Prediction (NWP) and urban energy-water balance models to study localized
phenomena such as the Urban Heat Island (UHI) effect. GLOBUS has been trained
and validated using the United States Geological Survey (USGS) 3DEP Light
Detection and Ranging (LiDAR) data. We used data from 5 US cities for training
and the model was validated over 6 cities. Performance metrics are computed at
a spatial resolution of 300-meter. The Root Mean Squared Error (RMSE) and Mean
Absolute Percentage Error (MAPE) were 5.15 meters and 28.8 %, respectively. The
standard deviation and histogram of building heights over a 300-meter grid are
well represented using GLOBUS.
