Intelligence artificielle pour la visualisation des changements de couverture terrestre en Chine centrale

Auteurs

DOI :

https://doi.org/10.47456/geo.v5i41.50748

Mots-clés :

teledetection, apprentissage automatique, traitement d'images

Résumé

Les données de télédétection (TD) constituent une source d'information essentielle pour la cartographie de la dynamique des paysages en milieu urbain. Les algorithmes d'intelligence artificielle (IA), notamment l'apprentissage automatique (AA), offrent des méthodes robustes pour le traitement de ces données. Cette étude a utilisé les méthodes d'AA du logiciel GRASS GIS pour traiter des images satellitaires et analyser les changements paysagers en Chine centrale. L'objectif est d'analyser la dynamique des paysages à travers les changements de couverture terrestre détectés sur une période de 10 ans, avec un intervalle de 2 ans. Le flux de travail comprenait l'algorithme d'AA Random Forest pour la classification d'images. Les données comprenaient six images Landsat 8-9 OLI/TIRS prises en automne en 2013, 2015, 2017, 2019, 2021 et 2023. Les résultats ont mis en évidence l'expansion de la ville de Wuhan, témoignant des processus d'urbanisation et d'aménagement intensif du territoire. Cet article démontre l'application d'une approche cartographique enrichie par l'IA à l'analyse d'images pour l'étude de la dynamique des paysages en Chine centrale.

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Biographie de l'auteur

  • Polina Lemenkova, University of Bologna

    Polina Lemenkova est cartographe et possède une solide expérience en analyse de données dans les sciences naturelles, l'ingénierie et les sciences techniques : https://orcid.org/0000-0002-5759-1089.

    Ses domaines de recherche comprennent la géoinformatique, l'apprentissage automatique et l'intelligence artificielle, le traitement des données de télédétection, les algorithmes de programmation (Python, R, GMT, GRASS GIS) et la géophysique. Elle est une auteure scientifique active, ayant publié des articles sur l'analyse d'images satellitaires, la surveillance et la cartographie environnementales, la géologie appliquée et la géographie. Voici quelques exemples de ses publications récentes :

    1. Lemenkova, P. (2025). Automation of image processing through ML algorithms of GRASS GIS using embedded Scikit-Learn library of Python. Examples and Counterexamples, 7(10):100180. https://doi.org/10.1016/j.exco.2025.100180
    2. Lemenkova, P. (2024). Artificial Intelligence for Computational Remote Sensing: Quantifying Patterns of Land Cover Types around Cheetham Wetlands, Port Phillip Bay, Australia. Journal of Marine Science and Engineering, 12(8), 1279. https://doi.org/10.3390/jmse12081279
    3. Lemenkova P. (2024). Artificial Neural Networks for Mapping Coastal Lagoon of Chilika Lake, India, Using Earth Observation Data. Journal of Marine Science and Engineering (12)5, 1-29. https://doi.org/10.3390/jmse12050709
    4. Lemenkova, P. (2024). Deep Learning Methods of Satellite Image Processing for Monitoring of Flood Dynamics in the Ganges Delta, Bangladesh. Water, 16(8), 1141. https://doi.org/10.3390/w16081141
    5. Lemenkova, P. (2025). Reclassification Scheme for Image Analysis in GRASS GIS Using Gradient Boosting Algorithm: A Case of Djibouti, East Africa. Journal of Imaging, 11(8), 249. https://doi.org/10.3390/jimaging11080249

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Relevo topográfico da China

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Publiée

15-12-2025

Numéro

Vol. 5 No 41 (2025): Revista Geografares

Rubrique

Articles

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(c) Copyright Geografares 2025

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Comment citer

Intelligence artificielle pour la visualisation des changements de couverture terrestre en Chine centrale. Geografares, Vitória, Brasil, v. 5, n. 41, p. e-50748, 2025. DOI: 10.47456/geo.v5i41.50748. Disponível em: https://periodicos.ufes.br/geografares/article/view/50748. Acesso em: 23 févr. 2026.