My research aims at narrowing the gap between optical remote sensing and applied marine ecology by developing novel techniques to map underwater ecosystems, with a special focus on macroalgal forests and seagrass meadows. The resulting cartographies represent the extent of key habitats and are therefore used to analyze underwater landscapes, infer ecological status and find early indicators of ecosystem collapses.

I like to use a multiscale approach, combining 3D underwater models with UAV and satellite data. To go from raw imagery to ecosystem maps, I explore several classification strategies, from manual digitalization and classic machine learning algorithms to neural networks. I particularly emphasize the importance of accurate field observations to validate artificial intelligence outcomes. Therefore, my studies involve not only capturing imagery and developing code for analysis, but also gathering species validation data with centimeter precision in intertidal and subtidal ecosystems.