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.

翻译：精确、详尽、高频次的水深数据，结合复杂的语义信息，对于正面临强烈气候与人为压力、测绘不足的浅海海床区域至关重要。当前利用遥感影像反演水深或海床类别的方法主要依赖于非公开数据。缺乏公开可访问的基准数据集阻碍了深度学习方法在此类应用中的更广泛使用。为解决这一问题，本文提出了MagicBathyNet，这是一个由Sentinel2、SPOT-6和航空影像的图像块、栅格格式的水深数据以及海床类别标注组成的基准数据集。随后，我们利用MagicBathyNet对基于学习的水深反演和像素级分类任务中的前沿方法进行了基准测试。数据集、预训练权重及代码已在www.magicbathy.eu/magicbathynet.html公开提供。