Inteligencia artificial para visualizar los cambios en la cobertura terrestre en el centro de China

Autores/as

DOI:

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

Palabras clave:

teledetección, aprendizaje automático, procesamiento de imágenes

Resumen

Los datos de teledetección (RS) son una fuente esencial de información para mapear la dinámica del paisaje en áreas urbanas. Los algoritmos de inteligencia artificial (IA), incluido el aprendizaje automático (ML), proporcionan métodos robustos para procesar datos de RS. Este estudio utilizó métodos ML del software GRASS GIS para procesar imágenes satelitales para analizar los cambios del paisaje en el centro de China. El objetivo es analizar la dinámica del paisaje a través de los cambios detectados en la cobertura terrestre durante 10 años con un intervalo de tiempo de 2 años. El flujo de trabajo incluyó el algoritmo Random Forest ML de clasificación de imágenes. Los datos incluyeron seis imágenes Landsat 8-9 OLI/TIRS tomadas en otoño en 2013, 2015, 2017, 2019, 2021 y 2023. Los resultados indicaron la expansión del área de la ciudad de Wuhan, lo que indica los procesos de urbanización y desarrollo intensivo de la tierra. Este artículo demostró la aplicación del enfoque mejorado por IA en cartografía al análisis de imágenes para el análisis de la dinámica del paisaje en el centro de China.

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Biografía del autor/a

  • Polina Lemenkova, Universidad de Bolonia

    Polina Lemenkova es cartógrafa con amplia experiencia en análisis de datos en ciencias naturales, ingeniería y técnicas: https://orcid.org/0000-0002-5759-1089.

    Sus áreas de investigación incluyen geoinformática, aprendizaje automático e inteligencia artificial, procesamiento de datos de teledetección, algoritmos de programación (Python, R, GMT, GRASS GIS) y geofísica. Es una activa escritora científica y ha publicado artículos sobre análisis de imágenes satelitales, monitoreo y cartografía ambiental, geología y geografía aplicadas. Entre sus publicaciones recientes se incluyen los siguientes artículos:

    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

Número

Vol. 5 Núm. 41 (2025): Revista Geografares

Sección

Articulos

Licencia

Derechos de autor 2025 Geografares

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Cómo citar

Inteligencia artificial para visualizar los cambios en la cobertura terrestre en el centro de China. 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: 4 jan. 2026.