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Filtros : "DIAS, HELEN CRISTINA" "IEE" "IGC" Removidos: "Ciência Ambiental" "CARBONO" "Viana, Camila Duelis" "IEE-PEE" Limpar

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  • Artigo de periodico

    Landslide Segmentation with Deep Learning: evaluating model generalization in rainfall-induced landslides in Brazil (2022)

    Soares, Lucas Pedrosa ; Dias, Helen Cristina ; Garcia, Guilherme Pereira Bento ; Grohmann, Carlos Henrique

    Source: Remote Sensing. Unidades: IEE, IGC

    Assunto: DESLIZAMENTO DE TERRA

    Versão PublicadaAcesso à fonteDOIHow to cite
    A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas

    • ABNT

      SOARES, Lucas Pedrosa et al. Landslide Segmentation with Deep Learning: evaluating model generalization in rainfall-induced landslides in Brazil. Remote Sensing, v. 14, n. 9, p. e2237/1-17, 2022Tradução . . Disponível em: https://doi.org/10.3390/rs14092237. Acesso em: 15 out. 2024.

    • APA

      Soares, L. P., Dias, H. C., Garcia, G. P. B., & Grohmann, C. H. (2022). Landslide Segmentation with Deep Learning: evaluating model generalization in rainfall-induced landslides in Brazil. Remote Sensing, 14( 9), e2237/1-17. doi:10.3390/rs14092237

    • NLM

      Soares LP, Dias HC, Garcia GPB, Grohmann CH. Landslide Segmentation with Deep Learning: evaluating model generalization in rainfall-induced landslides in Brazil [Internet]. Remote Sensing. 2022 ; 14( 9):e2237/1-17.[citado 2024 out. 15 ] Available from: https://doi.org/10.3390/rs14092237

    • Vancouver

      Soares LP, Dias HC, Garcia GPB, Grohmann CH. Landslide Segmentation with Deep Learning: evaluating model generalization in rainfall-induced landslides in Brazil [Internet]. Remote Sensing. 2022 ; 14( 9):e2237/1-17.[citado 2024 out. 15 ] Available from: https://doi.org/10.3390/rs14092237

  • Artigo de periodico

    Landslide recognition using SVM, Random Forest, and Maximum Likelihood classifiers on high-resolution satellite images: A case study of Itaóca, southeastern Brazil (2021)

    Dias, Helen Cristina ; Sandre, Lucas Henrique ; Satizábal Alarcón, Diego Alejandro ; Grohmann, Carlos Henrique ; Quintanilha, José Alberto

    Source: Brazilian Journal of Geology. Unidades: IEE, IGC

    Assunto: MUDANÇA CLIMÁTICA

    Acesso à fonteHow to cite
    A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas

    • ABNT

      DIAS, Helen Cristina et al. Landslide recognition using SVM, Random Forest, and Maximum Likelihood classifiers on high-resolution satellite images: A case study of Itaóca, southeastern Brazil. Brazilian Journal of Geology, v. 51, n. 4. p. e20200105/1-10, 2021Tradução . . Disponível em: https://www.scielo.br/j/bjgeo/a/Y6s5whm57BV9cgrMDWcJvgp/?format=pdf&lang=en. Acesso em: 15 out. 2024.

    • APA

      Dias, H. C., Sandre, L. H., Satizábal Alarcón, D. A., Grohmann, C. H., & Quintanilha, J. A. (2021). Landslide recognition using SVM, Random Forest, and Maximum Likelihood classifiers on high-resolution satellite images: A case study of Itaóca, southeastern Brazil. Brazilian Journal of Geology, 51( 4. p. e20200105/1-10). Recuperado de https://www.scielo.br/j/bjgeo/a/Y6s5whm57BV9cgrMDWcJvgp/?format=pdf&lang=en

    • NLM

      Dias HC, Sandre LH, Satizábal Alarcón DA, Grohmann CH, Quintanilha JA. Landslide recognition using SVM, Random Forest, and Maximum Likelihood classifiers on high-resolution satellite images: A case study of Itaóca, southeastern Brazil [Internet]. Brazilian Journal of Geology. 2021 ; 51( 4. p. e20200105/1-10):[citado 2024 out. 15 ] Available from: https://www.scielo.br/j/bjgeo/a/Y6s5whm57BV9cgrMDWcJvgp/?format=pdf&lang=en

    • Vancouver

      Dias HC, Sandre LH, Satizábal Alarcón DA, Grohmann CH, Quintanilha JA. Landslide recognition using SVM, Random Forest, and Maximum Likelihood classifiers on high-resolution satellite images: A case study of Itaóca, southeastern Brazil [Internet]. Brazilian Journal of Geology. 2021 ; 51( 4. p. e20200105/1-10):[citado 2024 out. 15 ] Available from: https://www.scielo.br/j/bjgeo/a/Y6s5whm57BV9cgrMDWcJvgp/?format=pdf&lang=en
  • 1

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