Portrait of Arthur Ouaknine

Arthur Ouaknine

Postdoctorate - McGill University
Supervisor
David Rolnick
Co-supervisor
Étienne Laliberté
Research Topics
Computer Vision
Deep Learning
Multimodal Learning
Website
Google Scholar
GitHub

Publications

SelvaBox: A high‑resolution dataset for tropical tree crown detection
Hugo Baudchon
Arthur Ouaknine
Martin Weiss
Mélisande Teng
Thomas Walla
Antoine Caron-Guay
Christopher Pal
Étienne Laliberté
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
openreview.net
Bringing SAM to new heights: Leveraging elevation data for tree crown segmentation from drone imagery
Mélisande Teng
Arthur Ouaknine
Étienne Laliberté
Yoshua Bengio
David Rolnick
Hugo Larochelle
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
NeurIPS.cc/2025/Conference (poster)
doi.org
openreview.net
GreenHyperSpectra: A multi-source hyperspectral dataset for global vegetation trait prediction
Eya Cherif
Arthur Ouaknine
Luke A. Brown
Phuong D. Dao
Kyle R. Kovach
Bing Lu
Daniel Mederer
Hannes Feilhauer
Teja Kattenborn
David Rolnick
Plant traits such as leaf carbon content and leaf mass are essential variables in the study of biodiversity and climate change. However, con… (see more)ventional field sampling cannot feasibly cover trait variation at ecologically meaningful spatial scales. Machine learning represents a valuable solution for plant trait prediction across ecosystems, leveraging hyperspectral data from remote sensing. Nevertheless, trait prediction from hyperspectral data is challenged by label scarcity and substantial domain shifts (\eg across sensors, ecological distributions), requiring robust cross-domain methods. Here, we present GreenHyperSpectra, a pretraining dataset encompassing real-world cross-sensor and cross-ecosystem samples designed to benchmark trait prediction with semi- and self-supervised methods. We adopt an evaluation framework encompassing in-distribution and out-of-distribution scenarios. We successfully leverage GreenHyperSpectra to pretrain label-efficient multi-output regression models that outperform the state-of-the-art supervised baseline. Our empirical analyses demonstrate substantial improvements in learning spectral representations for trait prediction, establishing a comprehensive methodological framework to catalyze research at the intersection of representation learning and plant functional traits assessment. All code and data are available at: https://github.com/echerif18/HyspectraSSL.
2025-09-17
NeurIPS.cc/2025/Datasets_and_Benchmarks_Track (poster)
doi.org
openreview.net
Assessing SAM for Tree Crown Instance Segmentation from Drone Imagery
Mélisande Teng
Arthur Ouaknine
Étienne Laliberté
Yoshua Bengio
David Rolnick
Hugo Larochelle
2025-03-25
ArXiv (preprint)
doi.org
arxiv.org
Tree semantic segmentation from aerial image time series
Venkatesh Ramesh
Arthur Ouaknine
David Rolnick
Earth's forests play an important role in the fight against climate change, and are in turn negatively affected by it. Effective monitoring … (see more)of different tree species is essential to understanding and improving the health and biodiversity of forests. In this work, we address the challenge of tree species identification by performing semantic segmentation of trees using an aerial image dataset spanning over a year. We compare models trained on single images versus those trained on time series to assess the impact of tree phenology on segmentation performances. We also introduce a simple convolutional block for extracting spatio-temporal features from image time series, enabling the use of popular pretrained backbones and methods. We leverage the hierarchical structure of tree species taxonomy by incorporating a custom loss function that refines predictions at three levels: species, genus, and higher-level taxa. Our findings demonstrate the superiority of our methodology in exploiting the time series modality and confirm that enriching labels using taxonomic information improves the semantic segmentation performance.
2024-12-31
Environmental Data Science (published)
doi.org
arxiv.org
Tackling Climate Change with Machine Learning: Fostering the Maturity of ML Applications for Climate Change
Shiva Madadkhani
Olivia Mendivil Ramos
Millie Chapman
Jesse Dunietz
Arthur Ouaknine
David Rolnick
Yoshua Bengio
2024-03-07
ICLR.cc/2024/Workshop_Proposals (published)
openreview.net
openreview.net
FoMo: Multi-Modal, Multi-Scale and Multi-Task Remote Sensing Foundation Models for Forest Monitoring
Nikolaos Ioannis Bountos
Arthur Ouaknine
Nikolaos Ioannis Bountos
David Rolnick
Forests are vital to ecosystems, supporting biodiversity and essential services, but are rapidly changing due to land use and climate change… (see more). Understanding and mitigating negative effects requires parsing data on forests at global scale from a broad array of sensory modalities, and using them in diverse forest monitoring applications. Such diversity in data and applications can be effectively addressed through the development of a large, pre-trained foundation model that serves as a versatile base for various downstream tasks. However, remote sensing modalities, which are an excellent fit for several forest management tasks, are particularly challenging considering the variation in environmental conditions, object scales, image acquisition modes, spatio-temporal resolutions, etc. With that in mind, we present the first unified Forest Monitoring Benchmark (FoMo-Bench), carefully constructed to evaluate foundation models with such flexibility. FoMo-Bench consists of 15 diverse datasets encompassing satellite, aerial, and inventory data, covering a variety of geographical regions, and including multispectral, red-green-blue, synthetic aperture radar and LiDAR data with various temporal, spatial and spectral resolutions. FoMo-Bench includes multiple types of forest-monitoring tasks, spanning classification, segmentation, and object detection. To enhance task and geographic diversity in FoMo-Bench, we introduce TalloS, a global dataset combining satellite imagery with ground-based annotations for tree species classification across 1,000+ categories and hierarchical taxonomic levels. Finally, we propose FoMo-Net, a pre-training framework to develop foundation models with the capacity to process any combination of commonly used modalities and spectral bands in remote sensing.
2023-12-14
ArXiv (preprint)
doi.org
arxiv.org
OpenForest: a data catalog for machine learning in forest monitoring
Arthur Ouaknine
Teja Kattenborn
Étienne Laliberté
David Rolnick
Forests play a crucial role in Earth's system processes and provide a suite of social and economic ecosystem services, but are significantly… (see more) impacted by human activities, leading to a pronounced disruption of the equilibrium within ecosystems. Advancing forest monitoring worldwide offers advantages in mitigating human impacts and enhancing our comprehension of forest composition, alongside the effects of climate change. While statistical modeling has traditionally found applications in forest biology, recent strides in machine learning and computer vision have reached important milestones using remote sensing data, such as tree species identification, tree crown segmentation and forest biomass assessments. For this, the significance of open access data remains essential in enhancing such data-driven algorithms and methodologies. Here, we provide a comprehensive and extensive overview of 86 open access forest datasets across spatial scales, encompassing inventories, ground-based, aerial-based, satellite-based recordings, and country or world maps. These datasets are grouped in OpenForest, a dynamic catalogue open to contributions that strives to reference all available open access forest datasets. Moreover, in the context of these datasets, we aim to inspire research in machine learning applied to forest biology by establishing connections between contemporary topics, perspectives and challenges inherent in both domains. We hope to encourage collaborations among scientists, fostering the sharing and exploration of diverse datasets through the application of machine learning methods for large-scale forest monitoring. OpenForest is available at https://github.com/RolnickLab/OpenForest .
2023-10-31
ArXiv (preprint)
doi.org
arxiv.org