MAGICBATHYNET: A Multimodal Remote Sensing Dataset for Bathymetry Prediction and Pixel-Based Classification in Shallow Waters
Panagiotis Agrafiotis, Łukasz Janowski, Dimitrios Skarlatos, Begüm Demir
Abstract
Accurate, detailed, and regularly updated bathymetry, coupled with complex semantic content, is crucial for the under-mapped shallow water areas facing intense climatological and anthropogenic pressures. Current methods exploiting remote sensing imagery to derive bathymetry or pixel-based 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 Sentinel-2, 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.