MagicBathyNet: A Multimodal Remote Sensing Dataset for Bathymetry Prediction and Pixel-based Classification in Shallow Waters
This provides a benchmark for researchers in remote sensing and marine science to advance deep learning applications in bathymetry and seabed mapping, though it is incremental as it focuses on dataset creation rather than novel method development.
The authors tackled the lack of openly accessible benchmark datasets for bathymetry prediction and seabed classification in shallow waters by introducing MagicBathyNet, a multimodal remote sensing dataset, and used it to benchmark state-of-the-art deep learning methods, with all resources made publicly available.
Accurate, detailed, and high-frequent bathymetry, coupled with complex semantic content, is crucial for the undermapped shallow seabed areas facing intense climatological and anthropogenic pressures. Current methods exploiting remote sensing images to derive bathymetry or seabed classes mainly exploit non-open data. This lack of openly accessible benchmark archives prevents the wider use of deep learning methods in such applications. To address this issue, in this paper we present the MagicBathyNet, which is a benchmark dataset made up of image patches of Sentinel2, SPOT-6 and aerial imagery, bathymetry in raster format and annotations of seabed classes. MagicBathyNet is then exploited to benchmark state-of-the-art methods in learning-based bathymetry and pixel-based classification. Dataset, pre-trained weights, and code are publicly available at www.magicbathy.eu/magicbathynet.html.