Satellite-derived bathymetry of Arabian Gulf ports under leakage-free validation: a multi-site benchmark of empirical, machine-learning and deep-learning models against 285,000 multibeam soundings, with an outlook for UAE waters
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Muhammad Saeed, Fatema Al Hajri, Mohamed H. Alhosani, Dmitry Mikhaylov, Yasser F. Al Wahedi · arXiv: 2610.33609v1 · Summary page
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The coastline looks different outside the training set
Estimating water depth from satellite images could make coastal monitoring cheaper, but familiar test procedures can flatter the technology. Researchers compared empirical formulas, machine learning and a neural-network ensemble against 285,619 multibeam soundings along the Abu Dhabi coast. For the same LightGBM model, error rose from 0.57 m under a random pixel split to 1.30 m with spatially separated testing and 1.66 m at an unseen site. In transfer to new sites, a simple band-ratio method was not beaten by the more elaborate alternatives.
Local measurements helped: a hybrid approach reached 1.11 m error with local control. But the seabed was not readable beyond 11 m, and performance depended on water clarity. The practical message extends well beyond mapping: randomly withholding observations does not necessarily test whether a model can work somewhere genuinely new. Here, satellite estimates look useful for reconnaissance and change monitoring, not as an unconditional substitute for survey-grade measurements.
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