From Cameras to Classifiers: The Role of Computer Vision in Citizen Science

Abstract

Computer vision has emerged as a transformative technology in citizen science, enabling automated analysis of images and videos at scales that would be impossible through human effort alone. This review examines the role of computer vision in citizen science, from camera trap projects that automatically capture wildlife images to mobile applications that provide real-time species identification. We analyze the technical approaches that have proven most effective, with particular focus on convolutional neural networks and transfer learning techniques that enable models to be trained on citizen science data. We examine applications across biodiversity monitoring, astronomy, medical imaging, and other domains, identifying success factors and limitations. We also address challenges including data quality, training data bias, model performance for rare species, and integration with volunteer workflows. We propose best practices for implementing computer vision in citizen science, including strategies for data collection, model training, validation, and volunteer engagement. We conclude by identifying emerging trends and future directions, including few-shot learning, multimodal approaches, and real-time analysis.