Abstract
We present the first curated dataset and deployable web/mobile prototype dedicated to the automatic identification of bird species recorded for the Galápagos Islands, a UNESCO World Heritage Site of exceptional ecological importance. The dataset was constructed through a multi-stage pipeline—automated retrieval, filtering, and checklist-guided multi-reviewer validation—using the Charles Darwin Foundation catalogue as a taxonomic reference. It comprises 6492 high-quality images covering 58 species, and represents the first publicly available resource specifically tailored to Galápagos checklist bird species. Leveraging this dataset, we benchmarked several state-of-the-art convolutional neural networks, including VGG16, ResNet50, EfficientNetB0, InceptionV3, MobileNetV2, and ConvNeXt, thereby establishing a reference baseline for future research. ConvNeXt obtained the best performance, exceeding 95% aggregate top-1 accuracy in the class-stratified image-level evaluation. Based on this model, we developed a web and mobile application that supports interactive species identification directly through the device’s camera. The system adapts content to three user profiles—children, tourists, and scientists—ensuring both accessibility and scientific rigor. This contribution combines the development of a dataset, benchmark, and deployable tool, aiming to foster biodiversity awareness, promote citizen science, and support conservation and educational initiatives in the Galápagos Islands.