Étienne Laliberté

Independent visiting researcher - Université de Montréal

Github

Sabrina Demers-Thibeault

Independent visiting researcher - Université de Montréal

Simon-Olivier Duguay

Master's Research - Université de Montréal

Github

Postdoctorate - McGill University

Principal supervisor :

Publications

Uncertainty Assessment in Deep Learning-based Plant Trait Retrievals from Hyperspectral data

Eya Cherif

Teja Kattenborn

Luke A. Brown

Michael Ewald

Katja Berger

Phuong D. Dao

Tobias B. Hank

Bing Lu

Hannes Feilhauer

Abstract. Large-scale mapping of plant biophysical and biochemical traits is essential for ecological and environmental applications. Given … (see more)their finer spectral resolution and unprecedented data availability, hyperspectral data, in concert with machine and particularly deep learning models, have emerged as a promising, non-destructive tool for accurately retrieving these traits. However, when deploying these methods on a large scale, reliably quantifying the associated uncertainty remains a critical challenge, especially when models encounter out-of-domain (OOD) data, i.e., samples that differ substantially from those of the training data, such as unseen geographical regions, species, biomes, data acquisition modalities, or scene components (e.g., clouds and water bodies). Traditional uncertainty quantification methods for deep learning models, including deep ensembles (deterministic and probabilistic) and Monte Carlo dropout, rely on the variance of predictions but often fail to capture uncertainty in OOD scenarios, leading to overly optimistic and possibly misleading uncertainty estimates. To address this limitation, we propose a distance-based uncertainty estimation method (Dis_UN) that quantifies prediction uncertainty by measuring the dissimilarity in the predictor space (spectral inputs) and embedding space (features learned by the deep model) between the training and test data. Dis_UN leverages residuals as a proxy for uncertainty and employs dissimilarity indices in data manifolds to estimate worst-case errors via 95-quantile regression. We evaluate Dis_UN using a pretrained deep learning model to predict multiple plant traits from hyperspectral images, analyzing its performance across OOD data, such as pixels containing spectral variations from urban surfaces, bare ground, water, clouds, or open surface waters. In this study, we target six leaf and canopy traits: leaf mass per area, chlorophylls, carotenoids, nitrogen content, equivalent water thickness, and leaf area index. Compared to scaled variance-based methods, Dis_UN provides (1) a superior estimation of uncertainty in OOD scenarios, achieving 36 % higher contrast (KS distances: 0.648 vs. 0.475) between non-vegetation pixels, particularly under mixed-pixel conditions at medium resolution (30 m); (2) uncertainty quantification without requiring normality or symmetry assumptions, accommodating asymmetric error patterns; (3) enhanced interpretability of uncertainty sources, as uncertainty is directly linked to sample dissimilarity from the training data; and (4) computational efficiency at inference (2.6–7.7× faster), requiring only a single forward pass compared to multiple passes for ensemble-based methods. Challenges remain for traits that are affected by spectral saturation. These findings highlight the advantages of distance-aware uncertainty quantification methods and underscore the necessity of diverse training datasets to minimize sampling biases and enhance model robustness. The proposed framework improves the reliability of uncertainty estimation in vegetation monitoring and offers a promising approach for broader applications.

2026-04-07

Biogeosciences (published)

Estimating Individual Tree Height and Species from UAV Imagery

J. Endres

David Rolnick

Accurate estimation of forest biomass, a major carbon sink, relies heavily on tree-level traits such as height and species. Unoccupied Aeria… (see more)l Vehicles (UAVs) capturing high-resolution imagery from a single RGB camera offer a cost-effective and scalable approach for mapping and measuring individual trees. We introduce BIRCH-Trees, the first benchmark for individual tree height and species estimation from tree-centered UAV images, spanning three datasets: temperate forests, tropical forests, and boreal plantations. We also present DINOvTree, a unified approach using a Vision Foundation Model (VFM) backbone with task-specific heads for simultaneous height and species prediction. Through extensive evaluations on BIRCH-Trees, we compare DINOvTree against commonly used vision methods, including VFMs, as well as biological allometric equations. We find that DINOvTree achieves top overall results with accurate height predictions and competitive classification accuracy while using only 54% to 58% of the parameters of the second-best approach.

2026-03-23

arXiv (preprint)

arxiv.org

PlantTraitNet: An Uncertainty-Aware Multimodal Framework for Global-Scale Plant Trait Inference from Citizen Science Data

Ayushi Sharma

Johanna Trost

Daniel Lusk

Johannes Dollinger

Julian Schrader

Christian Rossi

Javier Lopatin

Simon Haberstroh

Jana Eichel

Daniel Mederer

Jose Miguel Cerda-Paredes

Shyam S. Phartyal

Lisa-Maricia Schwarz

Anja Linstädter

Maria Conceição Caldeira

Teja Kattenborn

Global plant maps of plant traits, such as leaf nitrogen or plant height, are essential for understanding ecosystem processes, including the… (see more) carbon and energy cycles of the Earth system. However, existing trait maps remain limited by the high cost and sparse geographic coverage of field-based measurements. Citizen science initiatives offer a largely untapped resource to overcome these limitations, with over 50 million geotagged plant photographs worldwide capturing valuable visual information on plant morphology and physiology. In this study, we introduce PlantTraitNet, a multi-modal, multi-task uncertainty-aware deep learning framework that predicts four key plant traits (plant height, leaf area, specific leaf area, and nitrogen content) from citizen science photos using weak supervision. By aggregating individual trait predictions across space, we generate global maps of trait distributions. We validate these maps against independent vegetation survey data (sPlotOpen) and benchmark them against leading global trait products. Our results show that PlantTraitNet consistently outperforms existing trait maps across all evaluated traits, demonstrating that citizen science imagery, when integrated with computer vision and geospatial AI, enables not only scalable but also more accurate global trait mapping. This approach offers a powerful new pathway for ecological research and Earth system modeling.

2026-03-13

AAAI Conference on Artificial Intelligence (published)

arxiv.org

Understanding Representation Gaps across Scales in Tropical Tree Species Classification from Drone Imagery

Sulagna Saha

Carol Altimas

Evan M. Gora

Adriane Esquivel Muelbert

Ian R. McGregor

Cesar Gutierrez

Vanessa E. Rubio

David Rolnick

Accurate classification of tropical tree species from unoccupied aerial vehicle (UAV) imagery remains challenging due to high species divers… (see more)ity and strong visual similarity among species at typical image resolutions (centimeters per pixel). In contrast, models trained on close-up citizen science photographs captured with smartphones achieve strong plant species classification performance. Recent advances in UAV data acquisition now enable the collection of close-up images that are spatially registered with top-view aerial imagery and approach the level of visual detail found in smartphone photographs, with the trade-off that such high-resolution photos cannot be acquired for many trees. In this work, we evaluate the performance of existing methods using paired top-view and close-up UAV imagery collected in a species-rich tropical forest. Through fine-tuning experiments, we quantify the performance gap between vision foundation models and in-domain generalist plant recognition models across both image types (high-resolution close-up versus coarser-resolution top-view imagery). We show that classification performance is consistently higher on close-up images than on top-view aerial imagery, and that this performance gap widens for rare species. Finally, we propose that self-supervised representation alignment across these two spatial scales offers a promising approach for integrating fine-grained visual information into canopy-level species classification models based on top-view UAV imagery. Leveraging high-resolution close-up UAV imagery to enhance canopy-level species classification could substantially improve large-scale monitoring of tropical forest biodiversity.

2026-02-28

ML4RS @ International Conference on Learning Representations (published)

openreview.net

Seeing the forest and the trees: a workflow for automatic acquisition of ultra-high resolution drone photos of tropical forest canopies to support botanical and ecological studies

Vincent Le Falher

Guillaume Tougas

Helene C. Muller-Landau

Gonzalo Rivas-Torres

Thomas R. Walla

Hugo Baudchon

Melvin Hernández

Adrian Buenaño

Anna Weber

Jeffrey Q. Chambers

Jomber Chota Inuma

Fernando Araúz

Jorge Valdes

Andrés Hernández

David Brassfield

P. Sérgio

Vicente Vasquez

Adriana Simonetti … (see 7 more)

Daniel Magnabosco Marra

Caroline de Moura Vasconcelos

Jarol Fernando Vaca

Geovanny Rivadeneyra

José Illanes

Luis A. Salagaje-Muela

Jefferson Gualinga

Tropical forest canopies contain many tree and liana species, and foliar and reproductive characteristics useful for taxonomic identificatio… (see more)n are often difficult to see from the forest floor. As such, taxonomic identification often becomes a bottleneck in tropical forest inventories. Here we present a drone-based workflow to automatically acquire large volumes of close-up, ultra-high resolution photos of selected tree crowns (or specific locations over the canopy) to support tropical botanical and ecological studies ( https://youtu.be/80goMEifpc4 ). Our workflow is built around the small, easy-to-use DJI Mavic 3 Enterprise (M3E) drone, which is equipped with a wide-angle and a telephoto camera. On day one, the pilot maps a forest area of up to ∼200 ha with the wide-angle camera to generate a high-resolution digital surface model (DSM) and orthomosaic using structure-from-motion (SfM) photogrammetry. On subsequent days, the pilot acquires close-up photos with the telephoto camera from up to 300 selected canopy trees per day. These close-up photos are acquired from 6 m above the canopy and contain a high level of visual detail that allows botanists to reliably identify many tree and liana species. The photos are geolocated with survey-grade accuracy using RTK GNSS, thus facilitating spatial co-registration with other data sources, including the photogrammetry products. The primary operational challenge of our workflow is the need to maintain RTK corrections with the drone to ensure that close-up photos are acquired exactly at the predefined locations. The maximum operational range we achieved was 3 km, which would allow the pilot to reach any tree within a ∼2800 ha area from the take-off point. Although our workflow was developed to support taxonomic identification of tropical trees and lianas, it could be extended to any other forest or vegetation type to support botanical, phenological, and ecological studies. We provide harpia , an open-source Python library to program these automatic close-up photo missions with the M3E drone ( https://github.com/traitlab/harpia ). We provide harpia , an open-source Python library to program these automatic close-up photo missions ( https://github.com/traitlab/harpia ). Drone imagery and labelled close-up photo data are not yet publicly available because they were acquired with the goal of publishing benchmark machine learning datasets and models for tree and liana species classification and prior publication of the data would jeopardize this future publication.

2026-01-05

bioRxiv (preprint)

Mapping canopy foliar functional traits in a mixed temperate forest using imaging spectroscopy

Alice Gravel

Margaret Kalacska

Juan Pablo Arroyo-Mora

2026-01-03

Canadian Journal of Remote Sensing (published)

Additional methodological and statistical details from Testing the scale dependence of plant community assembly processes using imaging spectroscopy

Anna L. Crofts

J. Pablo Arroyo-Mora

Margaret Kalacska

Mark Vellend

Additional methodological details (including, Figure S1-S2) and detailed statistical results (Figure S3-S4 and Table S1-S3).

2025-12-31

Figshare (unknown)

SelvaBox: A high‑resolution dataset for tropical tree crown detection

Hugo Baudchon

Martin Weiss

Mélisande Teng

Thomas Walla

Christopher Pal

Detecting individual tree crowns in tropical forests is essential to study these complex and crucial ecosystems impacted by human interventi… (see more)ons and climate change. However, tropical crowns vary widely in size, structure, and pattern and are largely overlapping and intertwined, requiring advanced remote sensing methods applied to high-resolution imagery. Despite growing interest in tropical tree crown detection, annotated datasets remain scarce, hindering robust model development. We introduce SelvaBox, the largest open‑access dataset for tropical tree crown detection in high-resolution drone imagery. It spans three countries and contains more than

2025-12-31

International Conference on Learning Representations (Accept (Poster))

openreview.net

deadtrees.earth — An open-access and interactive database for centimeter-scale aerial imagery to uncover global tree mortality dynamics

Clemens Mosig

Janusch Vajna-Jehle

Miguel D. Mahecha

Yan Cheng

Henrik Hartmann

David Montero

Samuli Junttila

Stéphanie Horion

Mirela Beloiu Schwenke

Michael J. Koontz

Khairul Nizam Abdul Maulud

Stephen Adu-Bredu

Djamil Al-Halbouni

Muhammad Ali

Matthew Allen

Jan Altman

Lot Amorós

Claudia Angiolini

Rasmus Astrup

Hassan Awada … (see 80 more)

Caterina Barrasso

Harm Bartholomeus

Pieter S.A. Beck

Aurora Bozzini

Joshua Braun-Wimmer

Benjamin Brede

Fabio Marcelo Breunig

Stefano Brugnaro

Allan Buras

Vicente Burchard-Levine

Jesús Julio Camarero

Anna Candotti

Luka Capuder

Erik Carrieri

Mauro Centritto

Gherardo Chirici

Myriam Cloutier

Dhemerson Conciani

KC Cushman

James W. Dalling

Phuong D. Dao

Jan Dempewolf

Martin Denter

Marcel Dogotari

Ricardo Díaz-Delgado

Simon Ecke

Jana Eichel

Anette Eltner

André Fabbri

Maximilian Fabi

Fabian Fassnacht

Matheus Pinheiro Ferreira

Fabian Jörg Fischer

Julian Frey

Annett Frick

Jose Fuentes

Selina Ganz

Matteo Garbarino

Milton García

Matthias Gassilloud

Antonio Gazol

Guillermo Gea-Izquierdo

Kilian Gerberding

Marziye Ghasemi

Francesca Giannetti

Jeffrey Gillan

Roy Gonzalez

Carl Gosper

Terry Greene

Konrad Greinwald

Stuart Grieve

André Große-Stoltenberg

Jesus Aguirre Gutierrez

Anna Göritz

Peter Hajek

David Hedding

Jan Hempel

Stien Heremans

Melvin Hernández

Marco Heurich

Eija Honkavaara

Bernhard Höfle

Robert Jackisch

Tommaso Jucker

Jesse M. Kalwij

Sebastian Kepfer-Rojas

Pratima Khatri-Chhetri

Till Kleinebecker

Hans-Joachim Klemmt

Tomáš Klouček

Niko Koivumäki

Nagesh Kolagani

Jan Komárek

Kirill Korznikov

Bartłomiej Kraszewski

Stefan Kruse

Robert Krüger

Helga Kuechly

Ivan H.Y. Kwong

2025-10-23

Remote Sensing of Environment (published)

Bringing SAM to new heights: Leveraging elevation data for tree crown segmentation from drone imagery

Mélisande Teng

Information on trees at the individual level is crucial for monitoring forest ecosystems and planning forest management. Current monitoring … (see more)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.

2025-09-17

Neural Information Processing Systems (poster)

openreview.net

Assessing SAM for Tree Crown Instance Segmentation from Drone Imagery

Mélisande Teng

2025-03-25

ArXiv (preprint)

arxiv.org

Early Detection of an Invasive Alien Plant (Phragmites australis) Using Unoccupied Aerial Vehicles and Artificial Intelligence

Mickaël Germain

The combination of unoccupied aerial vehicles (UAVs) and artificial intelligence to map vegetation represents a promising new approach to im… (see more)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.

2024-06-05

bioRxiv (preprint)