ICML 2025

CoastalBench: A Decade-Long High-Resolution Dataset to Emulate Complex
Coastal Processes
Zelin Xu 1 Yupu Zhang 1 Tingsong Xiao 1 Maitane Olabarrieta 2 Jose M. Gonzalez-Ondina 2 Zibo Liu 1
Shigang Chen 1 Zhe Jiang 1
Abstract
Over 40% of the global population lives within
100 kilometers of the coast, which contributes
more than $8 trillion annually to the global
economy. Unfortunately, coastal ecosystems are
increasingly vulnerable to more frequent and
intense extreme weather events and rising sea
levels. Coastal scientists use numerical models to
simulate complex physical processes, but these
models are often slow and expensive. In recent
years, deep learning has become a promising
alternative to reduce the cost of numerical models.
However, progress has been hindered by the
lack of a large-scale, high-resolution coastal
simulation dataset to train and validate deep
learning models. Existing studies often focus on
relatively small datasets and simple processes. To
fill this gap, we introduce a decade-long, high-
resolution (<100m) coastal circulation modeling
dataset on a real-world 3D mesh in southwest
Florida with around 6 million cells. The dataset
contains
key
oceanography
variables
(e.g.,
current velocities, free surface level, temperature,
salinity) alongside external atmospheric and river
forcings.
We evaluated a customized Vision
Transformer model that takes initial and boundary
conditions and external forcings and predicts
ocean variables at varying lead times. The dataset
provides an opportunity to benchmark novel
deep learning models for high-resolution coastal
simulations (e.g., physics-informed machine
learning, neural operator learning). The code and
dataset can be accessed at https://github.
com/spatialdatasciencegroup/
CoastalBench.
1Department of Computer & Information Science & Engineer-
ing, University of Florida, Gainesville, FL, USA 2Department of
Civil & Coastal Engineering, University of Florida, Gainesville,
FL, USA. Correspondence to: Zhe Jia...