Data-driven Super-Resolution of Flood Inundation Maps using Synthetic Simulations

Akshay Aravamudan; Zimeena Rasheed; Xi Zhang; Kira E Scarpignato; Efthymios I Nikolopoulos; Witold F Krajewski; Georgios C Anagnostopoulos

doi:10.48550/arxiv.2502.10601

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Data-driven Super-Resolution of Flood Inundation Maps using Synthetic Simulations

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Data-driven Super-Resolution of Flood Inundation Maps using Synthetic Simulations

Akshay Aravamudan, Zimeena Rasheed, Xi Zhang, Kira E Scarpignato, Efthymios I Nikolopoulos, Witold F Krajewski and Georgios C Anagnostopoulos

ArXiV.org

Cornell University

02/14/2025

DOI: 10.48550/arxiv.2502.10601

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Preprint (Author's original)This preprint has not been evaluated by subject experts through peer review. Preprints may undergo extensive changes and/or become peer-reviewed journal articles. Open Access

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.

Computer Science - Computer Vision and Pattern Recognition

Computer Science - Learning

Details

Title: Subtitle: Data-driven Super-Resolution of Flood Inundation Maps using Synthetic Simulations
Creators: Akshay Aravamudan - Florida Institute of Technology
Zimeena Rasheed - Rutgers, The State University of New Jersey
Xi Zhang - Florida Institute of Technology
Kira E Scarpignato - Florida Institute of Technology
Efthymios I Nikolopoulos - Rutgers, The State University of New Jersey
Witold F Krajewski - University of Iowa
Georgios C Anagnostopoulos - Florida Institute of Technology
Resource Type: Preprint
Publication Details: ArXiV.org
DOI: 10.48550/arxiv.2502.10601
ISSN: 2331-8422
Publisher: Cornell University; Ithaca, New York
Language: English
Date posted: 02/14/2025
Academic Unit: Civil and Environmental Engineering
Record Identifier: 9984791030902771

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