The SMART-WING project is breaking new ground by developing a smart wing that can automatically switch between high and low altitude modes depending on the presence of animals. Thanks to its on-board artificial intelligence, the wing detects megafauna in real time, automatically descends to capture high-resolution images, and then resumes flight. This system will enable accurate data to be collected over large areas in an autonomous, efficient and environmentally friendly manner.

marine biology, biological oceanography

Launched in 2024 for three years and coordinated by Mounia Tahiri (Mohammed V University in Rabat), JEAI PESAS is developing a drought early-warning tool for Morocco. Machine learning models fuse satellite imagery, historical records and field measurements to forecast groundwater levels and quality several months ahead, starting with the Bouregreg basin, with support from IRD units G-EAU, IGE and UMMISCO.

hydrogeology, hydrology, machine learning, water

"The rise of machine learning allows us to consider a completely different approach, without any physical assumptions, based solely on empirical data. The idea is to feed the algorithms with all the quantitative observations of natural phenomena that we can muster, to see if they can identify factors that have so far escaped the attention of specialists."

geophysics, geodynamics, artificial intelligence

There is an important gap between biodiversity research and the management of natural areas. This research project aims to reduce this gap by proposing spatial planning methods that robustly and accurately integrate socio-ecological issues. Artificial intelligence will play a central role and will make it possible to remove the methodological obstacles that prevent us from properly addressing the complexity and heterogeneity of sustainability issues in the management of ecosystems.

biology, artificial intelligence

Predomics is a new machine learning approach designed for metagenomic data, which addresses the lack of interpretability in current predictive models for microbiome biomarkers. Inspired by interactions within microbial ecosystems, it bases its decisions on a simple score, obtained by adding, subtracting or dividing the cumulative abundances of microbiome measurements.

nutrition, environmental biotechnology, genomics

The AIME project utilises artificial intelligence to integrate heterogeneous data — satellite imagery, underwater images and videos, environmental DNA, GPS coordinates and telemetry — and to produce marine biodiversity indicators at different scales. The methods combine deep learning, ontologies and knowledge bases, natural language processing, machine learning and dynamic Bayesian networks, with a hybridisation of symbolic and sub-symbolic approaches aimed at enhancing the robustness and accuracy of the indicators.

marine biology, bioindicators, oceanography, artificial intelligence

The Nawras project utilises artificial intelligence to assess the law's ability to protect the marine environment. It is developing a methodology that combines legal science and AI to identify indicators for measuring 'where, when and how the law protects the oceans', with artificial intelligence automating the extraction of legal information.

law, marine biology, oceanography, artificial intelligence

On 19 May 2025 in Cañete, Peru, the sirens sounded even before the tremors: a first for the country. This early-warning system, designed by the IRD and the Instituto Geofísico del Perú, uses just a single seismic station — as soon as the first seismic waves are detected, AI algorithms estimate the magnitude and location within seconds, then issue an automatic alert. Just a few seconds, but enough time to get out of a building or reach a safe area, and thus to save lives.

detectors, earthquakes, tsunamis