Inteligência artificial para visualizar mudanças na cobertura do solo na China central

Autores

DOI:

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

Palavras-chave:

sensoriamento remoto, aprendizado de máquina, processamento de imagens

Resumo

Os dados de sensoriamento remoto (SR) são uma fonte essencial de informação para o mapeamento da dinâmica da paisagem em áreas urbanas. Algoritmos de inteligência artificial (IA), incluindo aprendizado de máquina (AM), fornecem métodos robustos para o processamento de dados de SR. Este estudo utilizou métodos de AM do software GRASS GIS para processar imagens de satélite e analisar as mudanças na paisagem da região central da China. O objetivo foi analisar a dinâmica da paisagem por meio das mudanças na cobertura do solo detectadas ao longo de 10 anos, com intervalos de 2 anos entre as imagens. O fluxo de trabalho incluiu o algoritmo Random Forest de classificação de imagens. Os dados incluíram seis imagens Landsat 8-9 OLI/TIRS, capturadas no outono de 2013, 2015, 2017, 2019, 2021 e 2023. Os resultados indicaram a expansão da área da cidade de Wuhan, o que evidencia os processos de urbanização e desenvolvimento intensivo do solo. Este artigo demonstra a aplicação de uma abordagem aprimorada por IA em cartografia para análise de imagens, visando a análise da dinâmica da paisagem na região central da China.

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Biografia do Autor

  • Polina Lemenkova, Università di Bologna

    Polina Lemenkova é cartógrafa com vasta experiência em análise de dados nas áreas de ciências naturais, engenharia e tecnologia: https://orcid.org/0000-0002-5759-1089.

    Suas áreas de pesquisa incluem geoinformática, aprendizado de máquina e inteligência artificial, processamento de dados de sensoriamento remoto, algoritmos de programação (Python, R, GMT, GRASS GIS) e geofísica. Ela é uma escritora científica ativa, autora de artigos sobre análise de imagens de satélite, monitoramento e mapeamento ambiental, geologia aplicada e geografia. Exemplos de seus trabalhos publicados recentemente incluem os seguintes artigos:

    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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Publicado

15-12-2025

Edição

v. 5 n. 41 (2025): Revista Geografares

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Artigos

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Inteligência artificial para visualizar mudanças na cobertura do solo na China central. 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: 7 jan. 2026.