Samuel Foucher

par
Samuel Foucher

Sujet de recherche

« Mes expertises principales portent sur les plateformes scientifiques pour l’exploitation des données massives en observation de la Terre ainsi que l’application de l’apprentissage profond aux sciences de l’environnement. J’ai plus de 25 ans d’expérience en recherche et développement en milieu industriel dans différents domaines d’application. Mes autres domaines de compétences touchent au traitement d’image et en particulier l’imagerie SAR, l’analyse multi-échelle, la détection d’objets appliquée à l’inventaire faunique, la fusion de données et l’apprentissage automatique appliquée aux graphes.« 

Courriel

Samuel.Foucher@USherbrooke.ca
+1 819 821-8000 x62286

Projets en cours

  • De l’observation de la Terre à distance aux services d’information décisionnelle (DOTS). Conseil de Recherches en Sciences Naturelles et Génie du Canada (CRSNG).
  • Data Analytics for Canadian Climate Services. Fondation Canadienne pour l’Innovation (FCI). Cyber-Infrastructure.
  • CANARIE – GeoImageNet : plateforme web pour l’annotation et l’apprentissage profond sur les images satellites à très haute résolution spatiale​ – transfert  technologique (collaboration avec Prof. Yacine Bouroubi)
  • Resolution Enhancement of Satellite Imagery using Deep Learning Techniques. Conseil de Recherches en Sciences Naturelles et Génie du Canada (CRSNG).

Publications (sélection d’articles)

  • Durand, S., Foucher, S., Delplanque, A., Taillon, J., & Théau, J. (2025). Lacking Data? No worries! How synthetic images can alleviate image scarcity in wildlife surveys: a case study with muskox (Ovibos moschatus). arXiv preprint arXiv:2511.11882.

  • Litalien, V., Duguay, J., Trudel, M., Foucher, S., Théau, J., & Fouquet, M. (2025). Assessing regression-based deep learning for river ice estimation from drone images. Cold Regions Science and Technology, 104656.

  • Delplanque, A., Linchant, J., Vincke, X., Lamprey, R., Théau, J., Vermeulen, C., Foucher, S., Ouattara, A., Kouadio, R. & Lejeune, P. (2024). Will artificial intelligence revolutionize aerial surveys? A first large-scale semi-automated survey of African wildlife using oblique imagery and deep learning. Ecological Informatics, 82, 102679.

  • Delplanque, A., Théau, J., Foucher, S., Serati, G., Durand, S., & Lejeune, P. (2024). Wildlife detection, counting and survey using satellite imagery: are we there yet?. GIScience & Remote Sensing, 61(1), 2348863.

  • Clabaut, É., Foucher, S., Bouroubi, Y., & Germain, M. (2024). A Self-Supervised Approach for Producing A Land Use Map for Southern Quebec. In IGARSS 2024-2024 IEEE International Geoscience and Remote Sensing Symposium (pp. 7049-7052). IEEE.

  • Clabaut, É., Foucher, S., Bouroubi, Y., & Germain, M. (2024). Synthetic data for sentinel-2 semantic segmentation. Remote Sensing, 16(5), 818.

  • Delplanque, A., Foucher, S., Théau, J., Bussière, E., Vermeulen, C., & Lejeune, P. (2023). From crowd to herd counting: How to precisely detect and count African mammals using aerial imagery and deep learning?. ISPRS Journal of Photogrammetry and Remote Sensing, 197, 167-180.

  • Moreni, M., Theau, J., & Foucher, S. (2023). Do you get what you see? Insights of using mAP to select architectures of pretrained neural networks for automated aerial animal detection. Plos one, 18(4), e0284449.

  • Delplanque, A., Lamprey, R., Foucher, S., Théau, J., & Lejeune, P. (2023). Surveying wildlife and livestock in Uganda with aerial cameras: Deep Learning reduces the workload of human interpretation by over 70%. Frontiers in Ecology and Evolution, 11, 1270857.

  • Ojaghi, S., Bouroubi, Y., Foucher, S., Bergeron, M., & Seynat, C. (2023). Deep Learning-Based Emulation of Radiative Transfer Models for Top-of-Atmosphere BRDF Modelling Using Sentinel-3 OLCI. Remote Sensing, 15(3), 835.

    • Tlili, A*; Cavayas, F; Foucher, S. (2021). A New Formulation of the Anisotropic Adaptive Gaussian Filter for Interferogram Filtering. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (Article soumis).
    • Turgeon-Pelchat, M*; Foucher, S; Bouroubi, Y. (2021). Deep Learning-based Classification of Large-scale Airborne Lidar Point Cloud for Land Cover Applications. Canadian Journal of Remote Sensing (Article soumis).
    • Delplanque, A*; Foucher, S; Lejeune, P; Linchant, J; Théau, J. (2021). Multispecies detection and identification of African mammals in aerial imagery using convolutional neural networks. Remote Sensing in Ecology and Conservation (Article accepté).
    • De la Sablonnière, S*; Foucher, S; Yacine, Y; Lord, É; Vigneault, P. (2021). Riparian Buffer Characterization in Agricultural Environments using Multi-View Deep Convolutional Neural Networks (MVDCNN) and Satellite Images. Remote Sensing of Environment (Article soumis).
    • Mael, M*; Theau, J; Foucher, S. (2021). Train fast while reducing false positives: improving animal classification performance using Convolutional Neural Networks. Geomatics (Révision demandée).
    • Dahmane M, Alam J, St-Charles PL, Lalonde, M, Heffner K, Foucher S. (2020). A Multimodal Non-Intrusive Stress Monitoring from the Pleasure-Arousal Emotional Dimensions. IEEE Transactions on Affective Computing (Article accepté).
    • Tlili, A*; Cavayas, F; Foucher, S; Siles, G. (2020). A New Interferometric Phase Unwrapping Method Based on Energy Minimization from Contextual Modeling. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 13 6524-6532. (Article accepté).
    • Boulent, J*; St-Charles, P-L; Foucher, S; Theau, J. (2020). Automatic Detection of Flavescence Dorée Symptoms Across White Grapevine Varieties Using Deep Learning. Frontiers in Artificial Intelligence 3 96. (Article publié).
    • Boulent J*, St-Charles P-L, Foucher S, Theau J. (2020). Automatic detection of Flavescence dorée symptoms across white grapevine varieties using deep learning. Frontiers in Artificial Intelligence (Article soumis).
    • Boulent, J*; Foucher, S; Théau, J; St-Charles, P-L*. (2019). Convolutional Neural Networks for the Automatic Identification of Plant Pests and Diseases : a Review. Frontiers in Plant Science, section Technical Advances in Plant Science 10 (Article publié).