Earth obserVation and machine lEarning foR aGRo-Environmental challENges
Earth obserVation and machine lEarning foR aGRo-Environmental challENges
The EVERGREEN team actively works on the design and implementation of cutting-edge machine learning techniques to effectively exploit heterogeneous and multi-temporal Earth observation data for numerous downstream tasks, including land cover mapping, land use following deforestation monitoring, forest variables estimation, and yield prediction to mention a few.
These endeavors directly address modern agro-environmental challenges with the goal to provide tools and insights for a more sustainable exploitation of natural resources.
To this end, the team delves into fundamental research questions related to the transferability of multi-modal classification models, the design of machine learning models for low-data regime scenarios, the interplay between model-centric and data-centric Machine Learning and the interpretability and explainability of classification algorithms for both image and time series data. This aspect is closely tied to the imperative of make the black box models gray, particularly within interdisciplinary research collaborations, such as the ones in which the EVERGREEN team operates daily.
Centre(s) inria
Inria Centre at Université Côte d'Azur
In partnership with


Team leader

Claire-Marine Parodi

Team assistant