Data-driven Super-Resolution of Flood Inundation Maps using Synthetic Simulations
Journal:
arXiv
Published Date:
Feb 14, 2025
Abstract
The frequency of extreme flood events is increasing throughout the world.
Daily, high-resolution (30m) Flood Inundation Maps (FIM) observed from space
play a key role in informing mitigation and preparedness efforts to counter
these extreme events. However, the temporal frequency of publicly available
high-resolution FIMs, e.g., from Landsat, is at the order of two weeks thus
limiting the effective monitoring of flood inundation dynamics. Conversely,
global, low-resolution (~300m) Water Fraction Maps (WFM) are publicly available
from NOAA VIIRS daily. Motivated by the recent successes of deep learning
methods for single image super-resolution, we explore the effectiveness and
limitations of similar data-driven approaches to downscaling low-resolution
WFMs to high-resolution FIMs. To overcome the scarcity of high-resolution FIMs,
we train our models with high-quality synthetic data obtained through
physics-based simulations. We evaluate our models on real-world data from flood
events in the state of Iowa. The study indicates that data-driven approaches
exhibit superior reconstruction accuracy over non-data-driven alternatives and
that the use of synthetic data is a viable proxy for training purposes.
Additionally, we show that our trained models can exhibit superior zero-shot
performance when transferred to regions with hydroclimatological similarity to
the U.S. Midwest.
