We, a collaborative team of machine learning scientists and ecologists entrusted with the management of biodiversity resources, are leveraging the power of interactive machine learning to drive impactful conservation strategies. Our endeavor will be published at the prestigious International Joint Conference on Artificial Intelligence (IJCAI).
The alarming acceleration of biodiversity loss demands innovative solutions and urgent action. At the forefront of this battle, biosphere reserves have emerged as crucial sites of global significance, harboring rich biodiversity and offering sustainable development opportunities. The management of biosphere reserves demands adaptive strategies that address the ongoing threats to biodiversity. However, the lack of comprehensive information on the status and trends of wildlife populations hinders effective decision-making. To bridge this knowledge gap, the research team delved into the realm of Passive Acoustic Monitoring (PAM).
Passive Acoustic Monitoring, a scalable and reproducible method for wildlife monitoring, has emerged as a promising avenue for closing the information gap in biodiversity conservation. By leveraging sound recordings, PAM enables continuous, non-invasive monitoring of animal wildlife across diverse ecosystems. Yet, the adoption of PAM has been hindered by challenges in data management and analysis, limiting its broader implementation by agencies with a mandate for biodiversity management. Recognizing the need for automation and streamlined analysis, our interdisciplinary team is developping state-of-the-art interactive machine learning tools tailored specifically for PAM data.
Our upcoming article, to be featured in the AI and Social Good section of the IJCAI conference proceedings, showcases research integrating advanced techniques like active learning and transfer learning. These techniques enable domain experts to enhance their work, a valuable and limited resource, by utilizing existing knowledge from databases to address specific and potentially novel scenarios encountered in different biosphere reserves. Complementing these advancements, our tools incorporate intuitive data visualization and intelligent user interfaces, enhancing the system’s usability and accessibility. This holistic approach equips conservation agencies with the necessary means to make data-driven decisions and drive effective conservation strategies.
H. Kath, T. S. Gouvêa, and D. Sonntag, ‘A Human-in-the-Loop Tool for Annotating Passive Acoustic Monitoring Datasets’, in Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, Macau, SAR China: International Joint Conferences on Artificial Intelligence Organization, Aug. 2023, pp. 7140–7144. doi: 10.24963/ijcai.2023/835.
T. S. Gouvêa et al., ‘Interactive Machine Learning Solutions for Acoustic Monitoring of Animal Wildlife in Biosphere Reserves’, in Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, Macau, SAR China: International Joint Conferences on Artificial Intelligence Organization, Aug. 2023, pp. 6405–6413. doi: 10.24963/ijcai.2023/711.