Deep CNNs with spatial continuity preservation and a new weighted loss function outperform Random Forest in cross-regional transfer for satellite-derived bathymetry, achieving low RMSE on independent tests and a public benchmark.
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2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2verdicts
UNVERDICTED 2representative citing papers
YOLO models trained on synthetic datasets for four particle shapes identify 2D colloidal assemblies with near-perfect accuracy on synthetic images but 43.1% average error on experimental images, varying by particle geometry.
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From Local Training to Large-Scale Mapping: A Comparative Assessment of Machine Learning and Deep Learning for Transferable Satellite-Derived Bathymetry
Deep CNNs with spatial continuity preservation and a new weighted loss function outperform Random Forest in cross-regional transfer for satellite-derived bathymetry, achieving low RMSE on independent tests and a public benchmark.
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Application of Machine Learning for the Identification of 2D Colloidal Assemblies: A Case Study on Particles of Distinct Shapes
YOLO models trained on synthetic datasets for four particle shapes identify 2D colloidal assemblies with near-perfect accuracy on synthetic images but 43.1% average error on experimental images, varying by particle geometry.