Saved in:
Bibliographic Details
Main Authors: 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
Format: Preprint
Published: 2026
Subjects:
Online Access:https://arxiv.org/abs/2603.10724
Tags: Add Tag
No Tags, Be the first to tag this record!
Table of 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.