OBJECTIVES

Tropical forests play a critical role in global biodiversity and carbon storage, yet their future under climate change remains highly uncertain due to the lack of scalable, species‑level monitoring tools. The Angèle St‑Pierre and Hugo Larochelle Research Chair in Artificial Intelligence for the Environment aims to fundamentally transform tropical forest observation by developing advanced AI models that leverage affordable, high‑resolution drone imagery to map, classify, and monitor individual trees across heterogeneous landscapes.

The research program is structured around three complementary pillars. First, it advances AI methods for tree‑level detection, segmentation, and classification, including the development of foundation and temporal models trained on large‑scale drone data. Second, these models are deployed to accelerate ecological science by producing high‑resolution maps of tree species distributions and phenological patterns across major neotropical research sites in Panama, Ecuador, and Brazil. Third, the program emphasizes real‑world impact by applying AI‑enabled forest monitoring to conservation, restoration assessment, and emerging biodiversity credit frameworks, in close collaboration with conservation organizations and Indigenous communities.

In 2026, the Chair will deliver foundational datasets, open AI models, and large‑scale canopy maps covering over 1,500 ha of tropical forest, while strengthening partnerships with global initiatives such as Pl@ntNet. By prioritizing openness and scalability, the program seeks to empower scientists, conservation practitioners, and local stakeholders with accessible, cutting‑edge tools to better understand, monitor, and protect tropical forests in a rapidly changing climate.