Visual WetlandBirds Dataset: Bird Species Identification and Behavior Recognition in Videos
Abstract The current biodiversity loss crisis makes animal monitoring a relevant field of study. In light of this, data collected through monitoring can provide essential insights, and information for decision-making aimed at preserving global biodiversity. Despite the importance of such data, there...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-07-01
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| Series: | Scientific Data |
| Online Access: | https://doi.org/10.1038/s41597-025-05516-5 |
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| _version_ | 1849238072123719680 |
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| author | Javier Rodriguez-Juan David Ortiz-Perez Manuel Benavent-Lledo David Mulero-Pérez Pablo Ruiz-Ponce Adrian Orihuela-Torres Jose Garcia-Rodriguez Esther Sebastián-González |
| author_facet | Javier Rodriguez-Juan David Ortiz-Perez Manuel Benavent-Lledo David Mulero-Pérez Pablo Ruiz-Ponce Adrian Orihuela-Torres Jose Garcia-Rodriguez Esther Sebastián-González |
| author_sort | Javier Rodriguez-Juan |
| collection | DOAJ |
| description | Abstract The current biodiversity loss crisis makes animal monitoring a relevant field of study. In light of this, data collected through monitoring can provide essential insights, and information for decision-making aimed at preserving global biodiversity. Despite the importance of such data, there is a notable scarcity of datasets featuring videos of birds, and none of the existing datasets offer detailed annotations of bird behaviors in video format. In response to this gap, our study introduces the first fine-grained video dataset specifically designed for bird behavior detection and species classification. This dataset addresses the need for comprehensive bird video datasets and provides detailed data on bird actions, facilitating the development of deep learning models to recognize these, similar to the advancements made in human action recognition. The proposed dataset comprises 178 videos recorded in Spanish wetlands, capturing 13 different bird species performing 7 distinct behavior classes. In addition, we also present baseline results using state of the art models on two tasks: bird behavior recognition and species classification. |
| format | Article |
| id | doaj-art-36ad4d17538d44eeb08ccc7f496299c3 |
| institution | Kabale University |
| issn | 2052-4463 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Data |
| spelling | doaj-art-36ad4d17538d44eeb08ccc7f496299c32025-08-20T04:01:47ZengNature PortfolioScientific Data2052-44632025-07-0112111310.1038/s41597-025-05516-5Visual WetlandBirds Dataset: Bird Species Identification and Behavior Recognition in VideosJavier Rodriguez-Juan0David Ortiz-Perez1Manuel Benavent-Lledo2David Mulero-Pérez3Pablo Ruiz-Ponce4Adrian Orihuela-Torres5Jose Garcia-Rodriguez6Esther Sebastián-González7Department of Computer Technology, University of AlicanteDepartment of Computer Technology, University of AlicanteDepartment of Computer Technology, University of AlicanteDepartment of Computer Technology, University of AlicanteDepartment of Computer Technology, University of AlicanteDepartment of Ecology, University of AlicanteDepartment of Computer Technology, University of AlicanteDepartment of Ecology, University of AlicanteAbstract The current biodiversity loss crisis makes animal monitoring a relevant field of study. In light of this, data collected through monitoring can provide essential insights, and information for decision-making aimed at preserving global biodiversity. Despite the importance of such data, there is a notable scarcity of datasets featuring videos of birds, and none of the existing datasets offer detailed annotations of bird behaviors in video format. In response to this gap, our study introduces the first fine-grained video dataset specifically designed for bird behavior detection and species classification. This dataset addresses the need for comprehensive bird video datasets and provides detailed data on bird actions, facilitating the development of deep learning models to recognize these, similar to the advancements made in human action recognition. The proposed dataset comprises 178 videos recorded in Spanish wetlands, capturing 13 different bird species performing 7 distinct behavior classes. In addition, we also present baseline results using state of the art models on two tasks: bird behavior recognition and species classification.https://doi.org/10.1038/s41597-025-05516-5 |
| spellingShingle | Javier Rodriguez-Juan David Ortiz-Perez Manuel Benavent-Lledo David Mulero-Pérez Pablo Ruiz-Ponce Adrian Orihuela-Torres Jose Garcia-Rodriguez Esther Sebastián-González Visual WetlandBirds Dataset: Bird Species Identification and Behavior Recognition in Videos Scientific Data |
| title | Visual WetlandBirds Dataset: Bird Species Identification and Behavior Recognition in Videos |
| title_full | Visual WetlandBirds Dataset: Bird Species Identification and Behavior Recognition in Videos |
| title_fullStr | Visual WetlandBirds Dataset: Bird Species Identification and Behavior Recognition in Videos |
| title_full_unstemmed | Visual WetlandBirds Dataset: Bird Species Identification and Behavior Recognition in Videos |
| title_short | Visual WetlandBirds Dataset: Bird Species Identification and Behavior Recognition in Videos |
| title_sort | visual wetlandbirds dataset bird species identification and behavior recognition in videos |
| url | https://doi.org/10.1038/s41597-025-05516-5 |
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