# BenthiCat: An opti-acoustic dataset for advancing benthic classification and habitat mapping

arXiv: https://arxiv.org/abs/2510.04876 · HTML: https://mlsanity.com/paper/2510.04876/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 35 references that are in the map.
- First posted on arXiv 6 Oct 2025.

## Builds on (most cited first, 4 of 4)

- [Machine Learning for Shipwreck Segmentation from Side Scan Sonar Imagery: Dataset and Benchmark](https://mlsanity.com/paper/2401.14546/) (2024)
- [A Convolutional Vision Transformer for Semantic Segmentation of Side-Scan Sonar Data](https://mlsanity.com/paper/2302.12416/) (2023)
- [SubPipe: A Submarine Pipeline Inspection Dataset for Segmentation and Visual-inertial Localization](https://mlsanity.com/paper/2401.17907/) (2024)
- [SeafloorAI: A Large-scale Vision-Language Dataset for Seafloor Geological Survey](https://mlsanity.com/paper/2411.00172/) (2024)

## Built on by (most cited first, 5 of 5)

- [PhysDNet: Physics-Guided Decomposition Network of Side-Scan Sonar Imagery](https://mlsanity.com/paper/2511.19539/) (2025)
- [Coarse-to-Fine Non-Rigid Registration for Side-Scan Sonar Mosaicking](https://mlsanity.com/paper/2512.00052/) (2025)
- [BenthicDINO: Physics-Informed Self-Distillation for View-Invariant Side-Scan Sonar Representations](https://mlsanity.com/paper/2608.23215/) (2026)
- [Geometry-Driven Opti-Acoustic Co-Registration and View-Invariant Reflectivity Mapping for Side-Scan Sonar](https://mlsanity.com/paper/2608.23479/) (2026)
- [Weakly Supervised Seafloor Segmentation for Seagrass Habitat Mapping in Side-Scan Sonar Imagery](https://mlsanity.com/paper/2608.24756/) (2026)

## Abstract (arXiv)

Benthic habitat mapping is fundamental for understanding marine ecosystems, guiding conservation efforts, and supporting sustainable resource management. Yet, the scarcity of large, annotated datasets limits the development and benchmarking of machine learning models in this domain. This paper introduces a thorough multi-modal dataset, comprising about a million side-scan sonar (SSS) tiles collected along the coast of Catalonia (Spain), complemented by bathymetric maps and a set of co-registered optical images from targeted surveys using an autonomous underwater vehicle (AUV). Approximately 36000 of the SSS tiles have been manually annotated with segmentation masks to enable supervised fine-tuning of classification models. All the raw sensor data, together with mosaics, are also released to support further exploration and algorithm development. To address challenges in multi-sensor data fusion for AUVs, we spatially associate optical images with corresponding SSS tiles, facilitating self-supervised, cross-modal representation learning. Accompanying open-source preprocessing and annotation tools are provided to enhance accessibility and encourage research. This resource aims to establish a standardized benchmark for underwater habitat mapping, promoting advancements in autonomous seafloor classification and multi-sensor integration.