Machine learning (ML) and conservation ecology is revolutionizing wildlife monitoring and ecological research. On 21 November 2022, the AI for Good platform hosted Dr. Devis Tuia, Head of the Environmental Computational Science and Earth Observation Lab in Sion. The webinar addressed the challenge of harnessing vast amounts of unstructured digital data in wildlife monitoring, demonstrating how AI-powered drone imaging systems and camera traps can automate animal detection and population estimation across expansive savannah landscapes with up to 90 per cent recall, a significant improvement over manual methods.
In order to address the challenge of detecting small animals in aerial imagery (averaging just 30.18 pixels) and navigating complex backgrounds, the webinar demonstrated the use of crowdsourced annotation platforms, which processed 26,000 images in a matter of days, as well as curriculum-based deep learning models pretrained on ImageNet. These models reduced false positives by 95 per cent, allowing rangers to analyse 300 photos rather than 1,500 during population surveys.
The webinar also illustrated how open-source tools such as AIDE (AI Interface for Ecological Data) can combine citizen science with ML, therefore allowing ecologists to create custom detection models without requiring programming skills.The webinar additionally highlighted how platforms such as Wildbook and iNaturalist can harness citizen science and social media data for ecological monitoring. This democratization of AI tools enables conservation practitioners to independently apply advanced ML methods, encouraging a critical synergy between ecological expertise and technical innovation. The webinar featured case studies on species identification in Namibia and migratory bird monitoring in West Africa, where AI cut analysis times from weeks to hours. It also introduced new and emerging methodologies such as taxonomy-aware models and spatio-temporal species distribution modelling to improve the precision of ecological decision-making. The webinar presented a scalable, replicable framework for global conservation efforts, tackling critical issues in real-time biodiversity monitoring and threat detection, such as poaching.
This case study is an excerpt from the AI for Good flagship report produced by UNU-CPR, Unlocking AI's Potential to Serve Humanity.