Portrait de Étienne Laliberté

Étienne Laliberté

Membre académique associé
Professeur titulaire, Université de Montréal, Département de sciences biologiques
Sujets de recherche
Vision par ordinateur

Biographie

Étienne Laliberté est professeur titulaire au Département de sciences biologiques de l'Université de Montréal, membre de l'Institut de recherche en biologie végétale (IRBV) et titulaire de la Chaire de recherche du Canada en biodiversité fonctionnelle végétale. Il dirige également l'Observatoire aérien canadien de la biodiversité (CABO).

Ses recherches actuelles se concentrent sur le développement de nouvelles approches pour la surveillance de la végétation (biodiversité végétale et carbone) basée sur la télédétection haute résolution à l'aide de drones et de la vision par ordinateur. Il s'intéresse particulièrement aux applications de cette technologie qui peuvent contribuer à atténuer la perte de biodiversité et le changement climatique, et qui peuvent avoir un effet rapide et généralisé.

Étudiants actuels

Maîtrise recherche - UdeM
Co-superviseur⋅e :
Visiteur de recherche indépendant - Université de Montréal
Visiteur de recherche indépendant - Université de Montréal
Maîtrise recherche - UdeM
Postdoctorat - McGill
Superviseur⋅e principal⋅e :

Publications

Bringing SAM to new heights: Leveraging elevation data for tree crown segmentation from drone imagery
Mélisande Teng
Etienne Lalibert'e
Information on trees at the individual level is crucial for monitoring forest ecosystems and planning forest management. Current monitoring … (voir plus)methods involve ground measurements, requiring extensive cost, time and labor. Advances in drone remote sensing and computer vision offer great potential for mapping individual trees from aerial imagery at broad-scale. Large pre-trained vision models, such as the Segment Anything Model (SAM), represent a particularly compelling choice given limited labeled data. In this work, we compare methods leveraging SAM for the task of automatic tree crown instance segmentation in high resolution drone imagery in three use cases: 1) boreal plantations, 2) temperate forests and 3) tropical forests. We also study the integration of elevation data into models, in the form of Digital Surface Model (DSM) information, which can readily be obtained at no additional cost from RGB drone imagery. We present BalSAM, a model leveraging SAM and DSM information, which shows potential over other methods, particularly in the context of plantations. We find that methods using SAM out-of-the-box do not outperform a custom Mask R-CNN, even with well-designed prompts. However, efficiently tuning SAM end-to-end and integrating DSM information are both promising avenues for tree crown instance segmentation models.
Bringing SAM to new heights: Leveraging elevation data for tree crown segmentation from drone imagery
Mélisande Teng
Etienne Lalibert'e
Information on trees at the individual level is crucial for monitoring forest ecosystems and planning forest management. Current monitoring … (voir plus)methods involve ground measurements, requiring extensive cost, time and labor. Advances in drone remote sensing and computer vision offer great potential for mapping individual trees from aerial imagery at broad-scale. Large pre-trained vision models, such as the Segment Anything Model (SAM), represent a particularly compelling choice given limited labeled data. In this work, we compare methods leveraging SAM for the task of automatic tree crown instance segmentation in high resolution drone imagery in three use cases: 1) boreal plantations, 2) temperate forests and 3) tropical forests. We also study the integration of elevation data into models, in the form of Digital Surface Model (DSM) information, which can readily be obtained at no additional cost from RGB drone imagery. We present BalSAM, a model leveraging SAM and DSM information, which shows potential over other methods, particularly in the context of plantations. We find that methods using SAM out-of-the-box do not outperform a custom Mask R-CNN, even with well-designed prompts. However, efficiently tuning SAM end-to-end and integrating DSM information are both promising avenues for tree crown instance segmentation models.
Assessing SAM for Tree Crown Instance Segmentation from Drone Imagery
Mélisande Teng
Etienne Lalibert'e
Assessing SAM for Tree Crown Instance Segmentation from Drone Imagery
Mélisande Teng
Etienne Lalibert'e
OpenForest: a data catalog for machine learning in forest monitoring
Teja Kattenborn
Etienne Lalibert'e
Early Detection of an Invasive Alien Plant (Phragmites australis) Using Unoccupied Aerial Vehicles and Artificial Intelligence
The combination of unoccupied aerial vehicles (UAVs) and artificial intelligence to map vegetation represents a promising new approach to im… (voir plus)prove the detection of invasive alien plant species (IAPS). The high spatial resolution achievable with UAVs and recent innovations in computer vision, especially with convolutional neural networks, suggest that early detection of IAPS could be possible, thus facilitating their management. In this study, we evaluated the suitability of this approach for mapping the location of common reed (Phragmites australis subsp. australis) within a national park located in southern Quebec, Canada. We collected data on six distinct dates during the growing season, covering environments with different levels of reed invasion. Overall, model performance was high for the different dates and zones, especially for recall (mean of 0.89). The results showed an increase in performance, reaching a peak following the appearance of the inflorescence in September (highest F1-score at 0.98). Furthermore, a decrease in spatial resolution negatively affected recall (18% decrease between a spatial resolution of 0.15 cm pixel−1 and 1.50 cm pixel−1) but did not have a strong impact on precision (2% decrease). Despite challenges associated with common reed mapping in a post-treatment monitoring context, the use of UAVs and deep learning shows great potential for IAPS detection when supported by a suitable dataset. Our results show that, from an operational point of view, this approach could be an effective tool for speeding up the work of biologists in the field and ensuring better management of IAPS.
Arbuscular and ectomycorrhizal tree seedling growth is inhibited by competition from neighboring roots and associated fungal hyphae
V. Parasquive
Jacques Brisson
P. L. Chagnon
Coordination among leaf and fine-root traits along a strong natural soil fertility gradient
Xavier Guilbeault-Mayers
Hans Lambers
Coordination among leaf and fine-root traits along a strong natural soil fertility gradient
Xavier Guilbeault-Mayers
Hans Lambers
Foliar spectra accurately distinguish most temperate tree species and show strong phylogenetic signal
Florence Blanchard
Anne Bruneau
Coordination among leaf and fine root traits across a strong natural soil fertility gradient
Xavier Guilbeault-Mayers
Hans Lambers
OpenForest: A data catalogue for machine learning in forest monitoring
Teja Kattenborn
Etienne Lalibert'e