At the AmazonIA – Mamirauá Meeting, held in the heart of the Amazon, international experts and institutions joined forces to define a new agenda for biodiversity monitoring. Our group contributed perspectives from interactive machine learning and large-scale acoustic monitoring—continuing the mission that earned us a top-3 result in the XPRIZE Rainforest competition.
From 8 to 10 September 2025, the Mamirauá Institute for Sustainable Development hosted the AmazonIA – Mamirauá Meeting in Tefé (Amazonas, Brazil)—a landmark gathering uniting scientists, conservation practitioners, technologists, and philanthropic organizations to shape the future of biodiversity monitoring in the Amazon. The meeting culminated in the Mamirauá Declaration, a joint commitment by more than thirty institutions to develop and deploy advanced technologies for long-term conservation of the Amazon rainforest and its peoples.
The event’s focus on integrating remote sensing, bioacoustics, and artificial intelligence resonated strongly with our own research on interactive machine learning for wildlife monitoring. Our work aims to bridge automation and expert knowledge in the analysis of large, unlabeled acoustic datasets, improving the detection and classification of animal vocalizations while keeping humans meaningfully involved in the learning process. These methods form the foundation of our ongoing development of open, collaborative tools for ecological monitoring—approaches that have been recognized internationally, including our Top-3 placement in the $10 M XPRIZE Rainforest competition.
The Mamirauá Declaration, drafted at the conclusion of the event, marks a significant step toward a shared international effort to monitor biodiversity and climate resilience in the Amazon. By linking scientific innovation with local stewardship, the initiative lays the groundwork for cross-border cooperation ahead of COP30 in Belém. Our group is honored to contribute to this collective endeavor, working alongside Brazilian and international partners to develop transparent, adaptive, and community-oriented AI tools for biodiversity observation at scale.