Since a year ago, the Senate's Foresight Delegation has been focusing on AI and the future of public services. Their work has resulted in the publication of several reports, including one dedicated to environmental issues (*), which was released a few days ago.
The good news is that among the projects and tools cited as examples is the PEP-BIOccIA research project, led by the Occitanie / Pyrénées-Méditerranée Region, in collaboration with OPenIG, CNRS, and our team.
Why this recognition? Because PEP-BIOccIA is one of the initiatives that meet a dual ambition:
- using AI to accelerate the ecological transition
- promoting the development of energy-efficient AI, which provides the desired level of performance while minimizing data usage, computing time, and resource consumption.
As a reminder, PEP-BIOccIA is a project that leverages AI to map natural environments across the entire Occitanie region and predict species presence in these various habitats.
The goal is to provide territorial planning stakeholders with comprehensive, annually updated data to better plan biodiversity conservation in the medium and long term.
Beyond this project, which started last fall, AI has been at the heart of our R&D efforts for four years.
This has led us, for example, to develop Ocapi, a software solution for automatic species recognition from camera trap images. AI performs the identification task, which can optionally be validated by a human. The results are then used to feed into other analyses, such as identifying collision risk zones between wildlife and vehicles.s.
On another front, the Polymor-FENCE research project, launched a year ago in partnership with CABINET X-AEQUO and the Laboratoire Image Ville Environnement (LIVE) UMR7362, aims to study the impacts of fences on wildlife.
AI is being used toAutomatically detect fences but also classify their types, to evaluate their impacts based on their structure and installation
The next step? The rise of digital twins for biodiversity, a concept we are already actively preparing for.
The goal of these tools already widely used in other industries—is to create a virtual replica of a real system, allowing us to predict its evolution and transformations over time.
AI will be a key component of this, working alongside modeling and sensor technologies.
(*) Read the full Senate report