BenthiCat: An opti-acoustic dataset for advancing benthic classification and habitat mapping
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2025
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866909843314442240 |
|---|---|
| author | Rajani, Hayat Franchi, Valerio Valles, Borja Martinez-Clavel Ramos, Raimon Garcia, Rafael Gracias, Nuno |
| author_facet | Rajani, Hayat Franchi, Valerio Valles, Borja Martinez-Clavel Ramos, Raimon Garcia, Rafael Gracias, Nuno |
| contents | 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 \num{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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_04876 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | BenthiCat: An opti-acoustic dataset for advancing benthic classification and habitat mapping Rajani, Hayat Franchi, Valerio Valles, Borja Martinez-Clavel Ramos, Raimon Garcia, Rafael Gracias, Nuno Computer Vision and Pattern Recognition Machine Learning I.2.6; I.4.6; I.5.1; I.5.4 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 \num{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. |
| title | BenthiCat: An opti-acoustic dataset for advancing benthic classification and habitat mapping |
| topic | Computer Vision and Pattern Recognition Machine Learning I.2.6; I.4.6; I.5.1; I.5.4 |
| url | https://arxiv.org/abs/2510.04876 |