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

• Published in: Data (Basel) Vol. 10; no. 6; p. 84
• Main Authors: Huang, Xiaoyuan, Mirri, Silvia, Tang, Su-Kit
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.06.2025
• Subjects: Accuracy; Annotations; Artificial intelligence; Biodiversity; Bird migration; Birds, Protection of; classification; Climate change; Conservation; Crowdsourcing; Datasets; detection; Endangered & extinct species; endangered bird; Endangered species; grounding DINO; Labeling; Localization; Macao-ebird dataset; Machine learning; Object recognition; Ornithology; R&D; Research & development; Taxonomy; Technology application; Urban environments; Wildlife conservation; YOLO; China; Macao
• ISSN: 2306-5729; 2306-5729
• DOI: 10.3390/data10060084

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.