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| Format: | Preprint |
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2026
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| Online-Zugang: | https://arxiv.org/abs/2603.10724 |
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| author | Beviá-Ballesteros, Ismael Jerez-Tallón, Mario Aranda-Garrido, Nieves Abel-Abellán, Isabel Antón-Linares, Irene Azorín-López, Jorge Saval-Calvo, Marcelo Fuster-Guilló, Andres Giménez-Casalduero, Francisca |
| author_facet | Beviá-Ballesteros, Ismael Jerez-Tallón, Mario Aranda-Garrido, Nieves Abel-Abellán, Isabel Antón-Linares, Irene Azorín-López, Jorge Saval-Calvo, Marcelo Fuster-Guilló, Andres Giménez-Casalduero, Francisca |
| contents | Elasmobranch populations are experiencing significant global declines, and several species are currently classified as threatened. Reliable monitoring and species-level identification are essential to support conservation and spatial planning initiatives such as Important Shark and Ray Areas (ISRAs). However, existing visual datasets are predominantly detection-oriented, underwater-acquired, or limited to coarse-grained categories, restricting their applicability to fine-grained morphological classification.
We present the eLasmobranc Dataset, a curated and publicly available image collection from seven ecologically relevant elasmobranch species inhabiting the eastern Spanish Mediterranean coast, a region where two ISRAs have been identified. Images were obtained through dedicated data collection, including field campaigns and collaborations with local fish markets and projects, as well as from open-access public sources. The dataset was constructed predominantly from images acquired outside the aquatic environment under standardized protocols to ensure clear visualization of diagnostic morphological traits. It integrates expert-validated species annotations, structured spatial and temporal metadata, and complementary species-level information.
The eLasmobranc Dataset is specifically designed to support supervised species-level classification, population studies, and the development of artificial intelligence systems for biodiversity monitoring. By combining morphological clarity, taxonomic reliability, and public accessibility, the dataset addresses a critical gap in fine-grained elasmobranch identification and promotes reproducible research in conservation-oriented computer vision. The dataset is publicly available at https://zenodo.org/records/18549737. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_10724 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | eLasmobranc Dataset: An Image Dataset for Elasmobranch Species Recognition and Biodiversity Monitoring Beviá-Ballesteros, Ismael Jerez-Tallón, Mario Aranda-Garrido, Nieves Abel-Abellán, Isabel Antón-Linares, Irene Azorín-López, Jorge Saval-Calvo, Marcelo Fuster-Guilló, Andres Giménez-Casalduero, Francisca Computer Vision and Pattern Recognition Elasmobranch populations are experiencing significant global declines, and several species are currently classified as threatened. Reliable monitoring and species-level identification are essential to support conservation and spatial planning initiatives such as Important Shark and Ray Areas (ISRAs). However, existing visual datasets are predominantly detection-oriented, underwater-acquired, or limited to coarse-grained categories, restricting their applicability to fine-grained morphological classification. We present the eLasmobranc Dataset, a curated and publicly available image collection from seven ecologically relevant elasmobranch species inhabiting the eastern Spanish Mediterranean coast, a region where two ISRAs have been identified. Images were obtained through dedicated data collection, including field campaigns and collaborations with local fish markets and projects, as well as from open-access public sources. The dataset was constructed predominantly from images acquired outside the aquatic environment under standardized protocols to ensure clear visualization of diagnostic morphological traits. It integrates expert-validated species annotations, structured spatial and temporal metadata, and complementary species-level information. The eLasmobranc Dataset is specifically designed to support supervised species-level classification, population studies, and the development of artificial intelligence systems for biodiversity monitoring. By combining morphological clarity, taxonomic reliability, and public accessibility, the dataset addresses a critical gap in fine-grained elasmobranch identification and promotes reproducible research in conservation-oriented computer vision. The dataset is publicly available at https://zenodo.org/records/18549737. |
| title | eLasmobranc Dataset: An Image Dataset for Elasmobranch Species Recognition and Biodiversity Monitoring |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2603.10724 |