eLasmobranc Dataset: An Image Dataset for Elasmobranch Species Recognition and Biodiversity Monitoring

2026-03-11

AI Summary

Existing visual datasets primarily focus on detection tasks, underwater imagery, or provide only coarse-grained taxonomic labels, limiting their utility for fine-grained identification and biodiversity monitoring of chondrichthyans. To address this gap, this work presents the first publicly available image dataset dedicated to seven ecologically relevant chondrichthyan species in the Eastern Mediterranean. Compiled through field surveys, market collaborations, and curated public resources, the dataset employs a standardized above-water imaging protocol to preserve clear morphological features. All annotations were validated by taxonomic experts and enriched with structured spatiotemporal metadata. This resource fills a critical data void for computer vision–enabled chondrichthyan conservation, enabling fine-grained classification, supervised learning, and reproducible, AI-driven biodiversity research. The dataset is openly accessible via the Zenodo repository.

Technology Category

• Computer Vision: Medical and Biological Imaging
• Humans and AI: Crowd Sourcing and Human Computation
• Data Mining & Knowledge Management: Mining of Visual, Multimedia & Multimodal Data

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Security and Privacy: Data transparency and provenance
• Responsible Web: Data and user privacy-enhancing technologies for the Web

Abstract

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.

Problem

• elasmobranch
• species recognition
• biodiversity monitoring
• fine-grained classification
• image dataset

Innovation

• fine-grained species classification
• elasmobranch dataset
• morphological trait visualization
• conservation-oriented computer vision
• expert-validated annotations