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

arXiv · 2502.10601

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

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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.

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BibTeX RIS

Akshay Aravamudan, Zimeena Rasheed, Xi Zhang, Kira E. Scarpignato, Efthymios I. Nikolopoulos, Witold F. Krajewski, Georgios C. Anagnostopoulos. 2025-02-14. Data-driven Super-Resolution of Flood Inundation Maps using Synthetic Simulations. https://arxiv.org/abs/2502.10601

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