OCAPI - Observation de la biodiversité par des caméras plus intelligentes | FEREC 2020

OCAPI – Observation de la biodiversité par des Caméras Plus Intelligentes Lauréat de l’appel à projets 20 20 Fondation FEREC « Intelligence artificielle (IA) appliquée aux infrastructures en service » 30 / 30 Burnham, K. P., and D. R. Anderson. 2002. Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach (2nd ed). Catalano, C., M. Meslec, J. Boileau, R. Guarino, I. Aurich, N. Baumann, F. Chartier, P. Dalix, S. Deramond, and P. Laube. 2021. Smart sustainable cities of the new millennium: towards design for nature. Circular Economy and Sustainability:1-34. Coulon, A., G. Guillot, J.-F. Cosson, J. M. A. Angibault, S. Aulagnier, B. Cargnelutti, M. Galan, and A. J. M. Hewison. 2006. Genetic structure is influenced by landscape features: empirical evidence from a roe deer population. Molecular Ecology 15:1669 – 1679. Duminil, A., J.-P. Tarel, and R. Brémond. 2021. Single Image Atmospheric Veil Removal Using New Priors. Pages 1719-1723 in 2021 IEEE International Conference on Image Processing (ICIP). IEEE. Fiske, I., and R. Chandler. 2011. Unmarked: an R package for fitting hierarchical models of wildlife occurrence and abundance. Journal Of Statistical Software 43:1-23. Lin, T.-Y., M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick. 2014. Microsoft COCO: Common Objects in Context. Pages 740-755 in D. Fleet, T. Pajdla, B. Schiele, and T. Tuytelaars, editors. Computer Vision – ECCV 2014. Springer International Publishing, Cham. Moulherat, S. 2014. Toward the development of predictive systems ecology modeling: MetaConnect and its use as an innovative modeling platform in theoretical and applied fields of ecological research. Université de Toulouse, Université Toulouse III-Paul Sabatier. Moulherat, S., E. Bestion, M. Baguette, M. Moulherat, S. C. F. Palmer, J. M. J. Travis, and J. Clobert. 2020. MetaConnect, a new platform for population viability modelling to assist decision makers in conservation and urban planning. bioRxiv. Moulherat, S., D. Le Roux, C. De Roince, M. Barbier, and C. Delran. 2019. BioBIM - Biodiversité, BIM et Infrastructures. FEREC. Norouzzadeh, M. S., A. Nguyen, M. Kosmala, A. Swanson, M. S. Palmer, C. Packer, and J. Clune. 2018. Automatically identifying, counting, and describing wild animals in camera-trap images with deep learning. Proceedings of the National Academy of Sciences 115:E5716-E5725. Noubissi, C. 2021. Reconnaissance et détection des Mammifères européens par apprentissage pour la sécurité routière. Université Gustave Eiffel, Paris. Pautrel, L. 2021. Using landscape genetics to evaluate the ability of models to reproduce the functioning of real metapopulations. Redmon, J., S. Divvala, R. Girshick, and A. Farhadi. 2016. You Only Look Once: Unified, Real-Time Object Detection. Pages 779-788 in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE. Rigoudy, N., A. Benyoub, A. Besnard, C. Birck, Y. Bollet, Y. Bunz, N. De Backer, G. Caussimont, A. Delestrade, and L. Dispan. 2022. The DeepFaune initiative: a collaborative effort towards the automatic identification of the French fauna in camera-trap images. bioRxiv. Tabak, M. A., M. S. Norouzzadeh, D. W. Wolfson, S. J. Sweeney, K. C. VerCauteren, N. P. Snow, J. M. Halseth, P. A. Di Salvo, J. S. Lewis, and M. D. White. 2019. Machine learning to classify animal species in camera trap images: Applications in ecology. Methods in Ecology and Evolution 10:585-590. Toli, A. M., and N. Murtagh. 2020. The concept of sustainability in smart city definitions. Frontiers in Built Environment 6:77. Valente, A. M., P. Acevedo, A. M. Figueiredo, C. Fonseca, and R. T. Torres. 2020. Overabundant wild ungulate populations in Europe: management with considerat ion of socio‐ecological consequences. Mammal Review 50:353-366.