A Large Scale Side-Scan Sonar Dataset of Seafloor Sediments for Self-Supervised Pretraining

This dataset serves as an extension to the dataset part of "A convolutional vision transformer for semantic segmentation of side-scan sonar data" published in Ocean Engineering, Volume 86, part 2, 15 October 2023, DOI: 10.1016/j.oceaneng.2023.115647 for self-supervised pretraining. This dataset consists of patches of side-scan sonar waterfalls collected along the coast of Catalunya during an extensive survey. The waterfalls were partitioned in batches of 384 lines to generate images of size 384 × 384 with a 192 pixel-overlap along-track and across-track. This resulted in a total of 434,164 images capturing various seafloor types including rocky bottoms, sand ripples, detrital funds, posidonia, cymocea, mud, corals, artificial reefs etc. Additional tools for using the data for self-supervised pretraining can be found under https://github.com/DeeperSense/deepersense-seafloorscan Acknowledgements The data in this repository were collected by Tecnoambiente SL as part of the project DeeperSense that received funding from the European Commission. Program H2020-ICT-2020-2 ICT-47-2020. Project Number: 101016958.