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Leveraging AI for Large-Scale Acoustic Biodiversity Monitoring: Insights from TABMON
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Webinar with Benjamin Cretois, Norwegian Institute for Nature Research. Advancing biodiversity monitoring is crucial for meeting the EU Biodiversity Strategy targets and addressing gaps in current ecological assessments. However, collecting data to monitor the state of biodiversity is time and resource consuming. Passive Acoustic Monitoring (PAM), in combination with AI tools offers an efficient alternative to conventional data collection practices. PAM is a non-invasive method that uses sound recorders to capture wildlife vocalizations and environmental sounds over time. It is particularly valuable for monitoring elusive or nocturnal species, such as birds, amphibians, and marine mammals, that are challenging to detect visually. The "Towards a Transnational Acoustic Biodiversity Monitoring Network" (TABMON) project is an initiative to establish a transnational passive acoustic monitoring monitoring network using autonomous acoustic sensors across four different European countries: Norway, Netherlands, France and Spain. TABMON’s objective is to demonstrate how acoustic sensing, coupled with cutting-edge AI, can complement traditional monitoring methods and support the development of methods to better monitor biodiversity. In this talk, we will also share our experiences with the deployment of acoustic recorders, data management strategies, and annotation protocols. These include managing large-scale, networked deployments across diverse landscapes, designing an efficient annotation workflow, and leveraging AI tools to process and analyze massive datasets.
December 2024
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Welcome to the Climate Change AI Nordics Newsletter, December 2024! Read about recent and coming seminars, workshops, and the modelling of thermal inequalities in African cities.
Upcoming seminars
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Climate AI Nordics will be hosting many seminars in 2025, featuring leading researchers exploring the role of AI in addressing climate change. Some speakers in the first quarter include Amal Nammouchi on leveraging large language models and deep reinforcement learning for trustworthy decision-making in energy management. Abdulhakim Abdi on the use of AI and Earth observation data to monitor ecosystems amid the biodiversity crisis. Atakan Aral on AI-driven environmental monitoring systems, and Sherrie Wang on AI applications in sustainable agriculture and climate mitigation. María J. Molina on AI's potential to predict extreme weather and inform climate strategies. Together uniting diverse perspectives on AI-driven climate solutions.
Leveraging AI for Climate Resilience in Africa: Challenges, Opportunities, and the Need for Collaboration
Event date:
Webinar with Amal Nammouchi, Karlstad University and AfriClimate AI. As climate change issues become more pressing, their impact in Africa calls for urgent, innovative solutions tailored to the continent’s unique challenges. While Artificial Intelligence (AI) emerges as a critical and valuable tool for climate change adaptation and mitigation, its effectiveness and potential are contingent upon overcoming significant challenges such as data scarcity, infrastructure gaps, and limited local AI development. This talk explores the role of AI in climate change adaptation and mitigation in Africa. It advocates for a collaborative approach to build capacity, develop open-source data repositories, and create context-aware, robust AI-driven climate solutions that are culturally and contextually relevant.