SATELLITE-DERIVED BATHYMETRY USING CONVOLUTIONAL NEURAL NETWORKS AND MULTISPECTRAL SENTINEL-2 IMAGES

12 citations as recorded by crossref.

1. Machine and deep learning methods for satellite-derived bathymetric mapping in Canadian coastal waters C. Ngalande & C. Armenakis https://doi.org/10.1016/j.geomat.2025.100088
2. Predicting beach profiles with machine learning from offshore wave reflection spectra E. Disdier et al. https://doi.org/10.1016/j.envsoft.2024.106221
3. Seabed-Net: A multi-task network for joint bathymetry estimation and seabed classification from remote sensing imagery in shallow waters P. Agrafiotis & B. Demir https://doi.org/10.1016/j.isprsjprs.2025.11.007
4. Satellite derived bathymetry using empirical and machine learning approaches: a case study in the highly dynamic coastal water N. Hao Quang et al. https://doi.org/10.1080/21664250.2024.2445418
5. A Review of Image- and LiDAR-Based Mapping of Shallow Water Scenarios P. Kujawa & F. Remondino https://doi.org/10.3390/rs17122086
6. Lyzenga Algorithm for Shallow Water Mapping Using Multispectral Sentinel-2 Imageries in Gili Noko Waters D. Pratomo et al. https://doi.org/10.1051/bioconf/20248907006
7. From Local Training to Large-Scale Mapping: A Comparative Assessment of Machine Learning and Deep Learning for Transferable Satellite-Derived Bathymetry H. Hsu & J. Moortgat https://doi.org/10.3390/rs18111768
8. Seabed Depth Prediction Using Multi-Scale Gravity Anomalies and Fully Connected Deep Neural Networks: A Novel Approach Applied to the South China Sea J. Yuan et al. https://doi.org/10.3390/rs17030412
9. Extracting Coastal Water Depths from Multi-Temporal Sentinel-2 Images Using Convolutional Neural Networks Y. Lumban-Gaol et al. https://doi.org/10.1080/01490419.2022.2091696
10. Incorporation of neighborhood information improves performance of SDB models A. Knudby & G. Richardson https://doi.org/10.1016/j.rsase.2023.101033
11. MuSRFM: Multiple scale resolution fusion based precise and robust satellite derived bathymetry model for island nearshore shallow water regions using sentinel-2 multi-spectral imagery X. Qin et al. https://doi.org/10.1016/j.isprsjprs.2024.09.007
12. Improving UAV-Derived Nearshore Bathymetry Using Geospatial Machine Learning T. Dias et al. https://doi.org/10.1109/JSTARS.2026.3721930