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Found 6 papers, 1 papers with code
CityTFT: Temporal Fusion Transformer for Urban Building Energy Modeling
Urban Building Energy Modeling (UBEM) is an emerging method to investigate urban design and energy systems against the increasing energy demand at urban and neighborhood levels.
Urban precipitation downscaling using deep learning: a smart city application over Austin, Texas, USA
We show the development of a high-resolution gridded precipitation product (300 m) from a coarse (10 km) satellite-based product (JAXA GsMAP).
Short-range forecasts of global precipitation using deep learning-augmented numerical weather prediction
We augment the output of the well-known NWP model CFSv2 with deep learning to create a hybrid model that improves short-range global precipitation at 1-, 2-, and 3-day lead times.
GLOBUS: GLObal Building heights for Urban Studies
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.
Deep learning for improved global precipitation in numerical weather prediction systems
This study is a proof-of-concept showing that residual learning-based UNET can unravel physical relationships to target precipitation, and those physical constraints can be used in the dynamical operational models towards improved precipitation forecasts.
Design and Deployment of Photo2Building: A Cloud-based Procedural Modeling Tool as a Service
We present a Photo2Building tool to create a plausible 3D model of a building from only a single photograph.
