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| Main Authors: | , , , , , , |
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| Format: | Preprint |
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2025
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| Online Access: | https://arxiv.org/abs/2502.10601 |
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| _version_ | 1866909495570989056 |
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| author | Aravamudan, Akshay Rasheed, Zimeena Zhang, Xi Scarpignato, Kira E. Nikolopoulos, Efthymios I. Krajewski, Witold F. Anagnostopoulos, Georgios C. |
| author_facet | Aravamudan, Akshay Rasheed, Zimeena Zhang, Xi Scarpignato, Kira E. Nikolopoulos, Efthymios I. Krajewski, Witold F. Anagnostopoulos, Georgios C. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_10601 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Data-driven Super-Resolution of Flood Inundation Maps using Synthetic Simulations Aravamudan, Akshay Rasheed, Zimeena Zhang, Xi Scarpignato, Kira E. Nikolopoulos, Efthymios I. Krajewski, Witold F. Anagnostopoulos, Georgios C. Computer Vision and Pattern Recognition Machine Learning I.4.9 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. |
| title | Data-driven Super-Resolution of Flood Inundation Maps using Synthetic Simulations |
| topic | Computer Vision and Pattern Recognition Machine Learning I.4.9 |
| url | https://arxiv.org/abs/2502.10601 |