Abstract
Monitoring vulnerable Houbara bustards, birds of both ecological and cultural significance, and detecting intruders that can pose a threat to their nests, is critical for effective conservation of these iconic species. Deep learning-based object detection offers an efficient solution for automating large-scale monitoring, yet its application to Houbara research has been hindered by the lack of comprehensive datasets. To address this gap, we present a new dataset of 24,318 camera-trap images, including 15,070 Houbara bustard images and 9,248 intruder images, all annotated with bounding boxes. Collected between 2011 and 2023 at various times of the day, and using 14 camera models, this dataset provides high diversity and complexity, enabling studies on Houbaras and other bustard species in similar habitats. We benchmarked 10 state-of-the-art object detection models, demonstrating that YOLOv10 outperforms others across evaluation metrics. This dataset represents a significant contribution to wildlife monitoring and conservation, supporting vulnerable Houbara bustard research while offering a foundation for broader applications by providing a valuable resource for wildlife researchers and practitioners.
Data availability
The dataset is available at figshare (https://doi.org/10.6084/m9.figshare.28202888.v1).
Code availability
The HBID24K dataset and associated codes for preprocessing, training, and evaluating object detection models are publicly available. The dataset annotations were created using the LabelMe tool, which is also open source and accessible online. Additionally, all the object detection models used for benchmarking are linked to their respective GitHub repositories, ensuring accessibility for researchers and practitioners. Table 10 provides a summary of the resources.
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Acknowledgements
This research has been supported by the International Fund for Houbara Conservation (IFHC) and Khalifa University under the project titled “AI-based Houbara Scene and Behavioral Understanding,” reference number 8434000545. Samples used in this study were provided by the International Fund for Houbara Conservation (IFHC). We are grateful to His Highness Sheikh Mohamed bin Zayed Al Nahyan, President of the United Arab Emirates and founder of the IFHC, His Highness Sheikh Theyab bin Mohamed Al Nahyan, Chairman of the IFHC, and His Excellency Mohammed Ahmed Al Bowardi, Deputy Chairman, for their support. This study was conducted under the guidance of Reneco International Wildlife Consultants LTD, a consulting company that manages the IFHC’s conservation programmes. We thank Dr Frédéric Lacroix, Managing Director of Reneco, for his supervision, as well as all staff of Reneco who participated in data collection, in particular Eric Le Nuz, head of Reneco Ecology Division, We also thank Dr Loic Lesobre, head of Reneco Genetic Division, and Dr Yves Hingrat, head of Reneco Research Division, for their scientific supervision.
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S.S.A. performed data preprocessing and benchmarking experiments. S.D.A. contributed to data annotations. S.J. conceptualized and supervised the study. I.H. and S.M. contributed to visualization and validation. T.D. and C.L. provided data, ecological insights, and expertise on the Houbara bustard. T.D. also revised the manuscript. E.S. provided data, ecological insights, expertise on Houbara bustard, and critical revision of the manuscript. N.W. provided critical revisions to the manuscript and technical expertise on object detection frameworks. All authors reviewed and approved the final manuscript.
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Ali, S.S., Ali, S.D., Javed, S. et al. HBID24K: A New Benchmark Dataset for Vulnerable Houbara Bustard and Intruder Detection in Wildlife Monitoring.
Sci Data (2026). https://doi.org/10.1038/s41597-025-06496-2
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DOI: https://doi.org/10.1038/s41597-025-06496-2
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