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Macao-ebird : A Curated Dataset for Artificial-Intelligence-Powered Bird Surveillance and Conservation in Macao

Author

Listed:
  • Xiaoyuan Huang

    (Faculty of Applied Sciences, Macao Polytechnic University, Macao SAR 999078, China
    School of Electrical Engineering and Intelligentization, Dongguan University of Technology, Dongguan 523000, China)

  • Silvia Mirri

    (Department of Computer Science and Engineering, University of Bologna, 40128 Bologna, Italy)

  • Su-Kit Tang

    (Faculty of Applied Sciences, Macao Polytechnic University, Macao SAR 999078, China)

Abstract

Artificial intelligence (AI) currently exhibits considerable potential within the realm of biodiversity conservation. However, high-quality regionally customized datasets remain scarce, particularly within urban environments. The existing large-scale bird image datasets often lack a dedicated focus on endangered species endemic to specific geographic regions, as well as a nuanced consideration of the complex interplay between urban and natural environmental contexts. Therefore, this paper introduces Macao-ebird , a novel dataset designed to advance AI-driven recognition and conservation of endangered bird species in Macao. The dataset comprises two subsets: (1) Macao-ebird-cls , a classification dataset with 7341 images covering 24 bird species, emphasizing endangered and vulnerable species native to Macao; and (2) Macao-ebird-det , an object detection dataset generated through AI-agent-assisted labeling using grounding DETR with improved denoising anchor boxes (DINO), significantly reducing manual annotation effort while maintaining high-quality bounding-box annotations. We validate the dataset’s utility through baseline experiments with the You Only Look Once (YOLO) v8–v12 series, achieving a mean average precision (mAP50) of up to 0.984. Macao-ebird addresses critical gaps in the existing datasets by focusing on region-specific endangered species and complex urban–natural environments, providing a benchmark for AI applications in avian conservation.

Suggested Citation

  • Xiaoyuan Huang & Silvia Mirri & Su-Kit Tang, 2025. "Macao-ebird : A Curated Dataset for Artificial-Intelligence-Powered Bird Surveillance and Conservation in Macao," Data, MDPI, vol. 10(6), pages 1-15, May.
    • Handle: RePEc:gam:jdataj:v:10:y:2025:i:6:p:84-:d:1668909
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    References listed on IDEAS

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    1. Sauermann, Henry & Vohland, Katrin & Antoniou, Vyron & Balázs, Bálint & Göbel, Claudia & Karatzas, Kostas & Mooney, Peter & Perelló, Josep & Ponti, Marisa & Samson, Roeland & Winter, Silvia, 2020. "Citizen science and sustainability transitions," Research Policy, Elsevier, vol. 49(5).
    2. W. Jetz & G. H. Thomas & J. B. Joy & K. Hartmann & A. O. Mooers, 2012. "The global diversity of birds in space and time," Nature, Nature, vol. 491(7424), pages 444-448, November.
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