César Leblanc

César Leblanc

he/him/comrade

Third year PhD student at Inria and University of Montpellier working on artificial intelligence and biodiversity within the Pl@ntNet team

Sitemap

A list of all the posts and pages found on the site. For you robots out there, there is an XML version available for digesting as well.

Pages

Posts

portfolio

publications

Species Distribution Modeling based on aerial images and environmental features with Convolutional Neural Networks

Published in Conference and Labs of the Evaluation Forum, 2022

This paper presents an approach to species distribution modeling using convolutional neural networks, integrating aerial imagery and environmental data to predict species presence across diverse geographical locations.

Recommended citation: Leblanc, C., Joly, A., Lorieul, T., Servajean, M., & Bonnet, P. (2022, September). Species Distribution Modeling based on aerial images and environmental features with Convolutional Neural Networks. In CLEF (Working Notes) (pp. 2123-2150).
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A deep-learning framework for enhancing habitat identification based on species composition

Published in Applied Vegetation Science, 2024

This paper introduces a novel deep-learning framework designed to improve habitat identification based on species composition, providing significant advancements over traditional expert systems.

Recommended citation: Leblanc, C., Bonnet, P., Servajean, M., Chytrý, M., Aćić, S., Argagnon, O., ... & Joly, A. (2024). A deep‐learning framework for enhancing habitat identification based on species composition. Applied Vegetation Science, 27(3), e12802.
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GeoPlant: Spatial Plant Species Prediction Dataset

Published in Conference on Neural Information Processing Systems, 2024

This paper presents GeoPlant, a novel European-scale dataset designed to advance species distribution modeling by integrating 5M Presence-Only records, 90k Presence-Absence surveys, environmental rasters, and high-resolution satellite imagery to predict over 10,000 plant species.

Recommended citation: Picek, L., Botella, C., Servajean, M., Leblanc, C., Palard, R., Larcher, T., ... & Joly, A. (2024, December). GeoPlant: Spatial Plant Species Prediction Dataset. In NEURIPS 2024.
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Fully automatic extraction of morphological traits from the Web: utopia or reality?

Published in Applications in Plant Sciences, 2024

This paper explores the feasibility of using large language models to automatically extract morphological traits of plants from unstructured online text, presenting a novel approach that achieves high accuracy in trait identification.

Recommended citation: Marcos, D., van de Vlasakker, R., Athanasiadis, I. N., Bonnet, P., Goëau, H., Joly, A., ... & Panousis, K. P. (2024). Fully automatic extraction of morphological traits from the web: Utopia or reality?. Applications in Plant Sciences, e70005.
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Mapping biodiversity at very-high resolution in Europe

Published in Conference on Computer Vision and Pattern Recognition, 2025

This paper describes a cascading multimodal pipeline for high-resolution biodiversity mapping across Europe, integrating species distribution modeling, biodiversity indicators, and habitat classification.

Recommended citation: Leblanc, C., Picek, L., Palard, R., Deneu, B., Servajean, M., Bonnet, P., & Joly, A. (2025). Mapping biodiversity at very-high resolution in Europe. In Proceedings of the Computer Vision and Pattern Recognition Conference (pp. 2349-2358).
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Learning the syntax of plant assemblages

Published in Nature Plants, 2025

This paper (currently in review) proposes a novel approach inspired by advances in large language models to learn the “syntax” of abundance-ordered plant species sequences in communities.

Recommended citation: Leblanc, C., Bonnet, P., Servajean, M., Thuiller, W., Chytrý, M., Aćić, S., ... & Joly, A. (2025). Learning the syntax of plant assemblages.
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talks

Other talks

In addition to the main conferences and congresses, I also had the opportunity to present my work in smaller talks and seminars.
Here is a list of some of them:

teaching

Other teachings

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In addition to the main teachings, I also had the opportunity to teach a lot of students with different backgrounds.
Here is a list of some of my experiences:

  • intensive preparatory courses to train students for enrolment in one of the grandes écoles (e.g., École Polytechnique and École Normale Supérieure)
  • accompanying students with difficult backgrounds and/or disabilities and preventing them from dropping out of the educational system (volunteer work)
  • helping collège and lycée students with homeworks and revisions so they keep up with their classes and obtain their diplomas (i.e., brevet and baccalauréat)
  • replacing my colleagues and/or supervisors when they are not available to teach their own university classes (e.g., Object-oriented programming)
  • participating in an association at my graduate school of engineering to help students struggling with certain courses (in particular in mathematics and physics)

Science des données 3 (2023-2024)

Licence 3, Département MIAp - Mathématiques et informatique appliquées, 2023

The goal of this course was to train data analysts capable of interpreting large volumes of data, with a particular focus on climate change. The students got familiar with some of the most important Python libraries to model and process problems linked to mathematics and IT (i.e., NumPy, Matplotlib, Pandas, seaborn, and GeoPandas). At the end of the course, they acquired:

  • a solid general scientific culture
  • the mastery of processing, modeling, and interpreting tools
  • analytical thinking, initiative, and teamwork skills

Science des données 3 (2024-2025)

Licence 3, Département MIAp - Mathématiques et informatique appliquées, 2024

The goal of this course was to train data analysts capable of interpreting large volumes of data, with a particular focus on climate change. The students got familiar with some of the most important Python libraries to model and process problems linked to mathematics and IT (i.e., NumPy, Matplotlib, Pandas, seaborn, and GeoPandas). At the end of the course, they acquired:

  • a solid general scientific culture
  • the mastery of processing, modeling, and interpreting tools
  • analytical thinking, initiative, and teamwork skills