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Data and code for "High-resolution digital bathymetric model reconstructed using water-air amphibious unmanned aerial vehicles and deep learning"
A newer version of this resource http://www.hydroshare.org/resource/8bb235a1de0b474d87502d25fc5e2292 is available that replaces this version.
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Created: Mar 22, 2026 at 12:56 a.m. (UTC)
Last updated: Nov 07, 2026 at 3:27 a.m. (UTC) (Metadata update)
Published date: Sep 03, 2026 at 6:19 p.m. (UTC)
DOI: 10.4211/hs.910e02d1e81b48bcb2ca87a7d499a525
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Sharing Status: Published
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Abstract

Digital bathymetric models (DBMs) are fundamental terrain inputs for hydrodynamic modelling and flood-emergency analysis, but rapid acquisition of high-resolution bathymetry remains challenging in complex and hazardous waters. This study develops a super-resolution (SR) reconstruction framework integrating amphibious unmanned aerial vehicles (UAVs), sonar measurements, and deep learning. High-density field sonar observations were spatially regularized to construct the reference high-resolution (HR) DBM, whereas realistic low-resolution (LR) inputs were generated by spatially thinning the original sonar soundings before rasterization, forming measurement-derived LR–HR pairs for 4× SR reconstruction. A topographic and spectral constrained Transformer GAN (TSCTGAN) was then developed by integrating lightweight Transformer attention, geometric and spectral constraints, and adaptive loss weighting based on homoscedastic uncertainty. On the test dataset, TSCTGAN achieved a PSNR of 46.39 dB and an RMSE of 0.177 m. Compared with Bicubic and Kriging reconstruction from the same sparse observations, PSNR increased by 2.33–4.56 dB and RMSE decreased by 51.9%–54.0%. Three extrapolation datasets representing cross-sensor, cross-resolution, and geographically independent cross-site conditions yielded TSCTGAN RMSEs of approximately 0.140, 0.063, and 0.113 m. For full-scene reconstruction, TSCTGAN achieved a depth RMSE of 0.136 m, a pairwise overlap RMSE of 0.021 m, and a seam depth-increment RMSE of 0.046 m. These results demonstrate that the proposed framework can retain favorable bathymetric accuracy and spatial continuity during patch-based full-scene reconstruction from sparse field observations. The framework addresses the bathymetric reconstruction stage preceding hydrodynamic modelling and provides a potential approach for rapidly generating spatially detailed DBM terrain inputs.

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Related Resources

This resource has been replaced by a newer version Tang, J. (2026). Data and code for "High-resolution digital bathymetric model reconstructed using water-air amphibious unmanned aerial vehicles and deep learning", HydroShare, http://www.hydroshare.org/resource/8bb235a1de0b474d87502d25fc5e2292

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Funding Agencies

This resource was created using funding from the following sources:
Agency Name Award Title Award Number
National Key Research and Development Program of China None 2024YFC3015900

How to Cite

Tang, J. (2026). Data and code for "High-resolution digital bathymetric model reconstructed using water-air amphibious unmanned aerial vehicles and deep learning", HydroShare, https://doi.org/10.4211/hs.910e02d1e81b48bcb2ca87a7d499a525, DOI for this published resource is pending activation.

This resource is shared under the Creative Commons Attribution CC BY.

 http://creativecommons.org/licenses/by/4.0/
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