IBERBIRDS: A dataset of flying bird species present in the Iberian PeninsulaZenodo
Advancements in computer vision and deep learning have transformed ecological monitoring and species identification, enabling automated and accurate data labelling. Despite these advancements, robust AI-driven solutions for avian species recognition remain limited, primarily due to the scarcity of h...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-06-01
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| Series: | Data in Brief |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352340925003427 |
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| Summary: | Advancements in computer vision and deep learning have transformed ecological monitoring and species identification, enabling automated and accurate data labelling. Despite these advancements, robust AI-driven solutions for avian species recognition remain limited, primarily due to the scarcity of high-quality annotated datasets. To address this gap, this article introduces IBERBIRDS—a comprehensive and publicly accessible dataset specifically designed to facilitate automatic detection and classification of flying bird species in the Iberian Peninsula under real-world conditions.The dataset comprises 4000 images representing 10 ecologically significant medium to large-sized bird species, with each image annotated using bounding box coordinates in the YOLO detection format. Unlike existing datasets that typically feature close-up or ideal-condition imagery, IBERBIRDS focuses on mid-to-long range photographs of birds in flight, providing a more realistic and challenging representation of scenarios commonly encountered in birdwatching, conservation, and ecological monitoring. Images were sourced from publicly available, expert-validated ornithology platforms and underwent rigorous quality control to ensure annotation accuracy and consistency. This process included homogenizing color profiles and formats, as well as manual refinement to ensure that each image contains a single bird specimen. Additionally, detailed provenance and taxonomic metadata for each image has been systematically integrated into the dataset.The lack of pre-annotated datasets has significantly restricted large-scale ecological analysis and the development of automated techniques in avian research, hindering the progress of AI-driven solutions tailored for bird species recognition. By addressing this gap, this dataset serves as a comprehensive benchmark for avian studies, fostering advancements in various applications such as conservation initiatives, environmental impact assessments, biodiversity preservation strategies, real-time tracking systems, and video-based analysis. Additionally, IBERBIRDS constitutes a resource for computer vision applications, supporting educational programs tailored to ornithologists and birdwatching communities. By openly providing this dataset, IBERBIRDS promotes scientific collaboration and technological advancements, ultimately contributing to the preservation and understanding of avian biodiversity. |
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| ISSN: | 2352-3409 |