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Data and code for "High-resolution digital bathymetric model reconstructed using water-air amphibious unmanned aerial vehicles and deep learning"
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| Type: | Resource | |
| Storage: | The size of this resource is 79.7 MB | |
| Created: | Mar 22, 2026 at 12:56 a.m. (UTC) | |
| Last updated: | Sep 03, 2026 at 6:19 p.m. (UTC) (Metadata update) | |
| Published date: | Sep 03, 2026 at 6:19 p.m. (UTC) | |
| DOI: | 10.4211/hs.910e02d1e81b48bcb2ca87a7d499a525 | |
| Citation: | See how to cite this resource |
| Sharing Status: | Published |
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| Views: | 41 |
| Downloads: | 3 |
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Abstract
Sub-meter DBM were collected using amphibious UAVs to establish a new database. A topographic and spectral constrained Transformer GAN (TSCTGAN) was developed for 4× SR reconstruction, which incorporates a lightweight Transformer attention mechanism, multi-physical constraints and dynamic weighting strategy based on homoscedastic uncertainty to enhance reconstruction accuracy. Results demonstrate that the performance of TSCTGAN outperforms other SR techniques such as ESRGAN, DDPM, and TfaSR. Compared to the Bicubic method, TSCTGAN achieves a 3.39 dB improvement in PSNR and reduces RMSE from 0.489 m to 0.291 m. Moreover, the model exhibits robust generalization capabilities on cross-equipment and cross-scale extrapolation datasets.
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Funding Agencies
This resource was created using funding from the following sources:
| Agency Name | Award Title | Award Number |
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| National Key Research and Development Program of China | None | 2024YFC3015900 |
How to Cite
This resource is shared under the Creative Commons Attribution CC BY.
http://creativecommons.org/licenses/by/4.0/
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