IA FR-DE - Type 1 CR - Appel à projets bilatéral franco-allemand en intelligence artificielle (MESRI-BMBF) - Type 1 Collaboration de Recherche 2022

Artificial Intelligence for forest monitoring from space – AI4Forests

AI4FOREST

https://peerj.com/articles/9750/#aff-12

AI4Forest aims at the application of scalable and efficient AI methods in the field of sustainabil- ity. It will, hence, have a direct impact on the national AI strategies of both France and Germany

Submission summary

Managing and conserving forest ecosystems in Europe and worldwide is an indispensable component of climate adaptation and climate change mitigation strategies. Precise and up-to-date information about the health and the carbon balance of forests are, hence, critical to assess the current state of forests, trigger appropriate countermeasures against forest loss, and develop improved management strategies. Advances in both Earth observation and artificial intelligence have paved the way for the automation of forest monitoring using satellite time series data, including optical, radar, and LiDAR measurements. The forest maps produced by today's approaches, however, are still often limited to coarse resolutions and/or to relatively small spatial areas. To overcome those limitations, the AI4Forest project brings together experts in artificial intelligence, applied mathematics, computer science, spatial remote sensing, and climate change. AI4Forest strives for both conceptually novel AI methods for forest monitoring as well as for scalable AI methods that allow to process large amounts of data efficiently and at low cost. The resulting techniques will facilitate the generation of detailed forest maps at a very high spatial and temporal resolution for the whole European continent and the entire world, including tree species identification, mortality and biomass carbon stocks changes down to the level of individual trees.

Project coordination

Philippe Ciais (Laboratoire des Sciences du Climat et de l'Environnement UMR 8212)

The author of this summary is the project coordinator, who is responsible for the content of this summary. The ANR declines any responsibility as for its contents.

Partnership

LSCE Laboratoire des Sciences du Climat et de l'Environnement UMR 8212
DIENS Département d'Informatique de l'Ecole Normale Supérieure
WWU Westfälische Wilhelms-Universität Münster
ZIB Zuse Institute Berlin
TUM TUM School of Lile Sciences

Help of the ANR 1,047,062 euros
Beginning and duration of the scientific project: May 2023 - 48 Months

Useful links

Explorez notre base de projets financés

 

 

ANR makes available its datasets on funded projects, click here to find more.

Sign up for the latest news:
Subscribe to our newsletter