Filtros : "IEE-DVCTECP-04" "2022" Limpar

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  • Source: Open Geosciences. Unidade: IEE

    Assunto: MONITORAMENTO AMBIENTAL

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

      ALBUQUERQUE, Rafael Walter et al. A protocol for canopy cover monitoring on forest restoration projects using low-cost drones. Open Geosciences, v. 14, n. 1, p. 921-929, 2022Tradução . . Disponível em: https://doi.org/10.1515/geo-2022-0406. Acesso em: 31 out. 2024.
    • APA

      Albuquerque, R. W., Matsumoto, M. H., Calmon, M., Ferreira, M. E., Vieira, D. L. M., & Grohmann, C. H. (2022). A protocol for canopy cover monitoring on forest restoration projects using low-cost drones. Open Geosciences, 14( 1), 921-929. doi:10.1515/geo-2022-0406
    • NLM

      Albuquerque RW, Matsumoto MH, Calmon M, Ferreira ME, Vieira DLM, Grohmann CH. A protocol for canopy cover monitoring on forest restoration projects using low-cost drones [Internet]. Open Geosciences. 2022 ; 14( 1): 921-929.[citado 2024 out. 31 ] Available from: https://doi.org/10.1515/geo-2022-0406
    • Vancouver

      Albuquerque RW, Matsumoto MH, Calmon M, Ferreira ME, Vieira DLM, Grohmann CH. A protocol for canopy cover monitoring on forest restoration projects using low-cost drones [Internet]. Open Geosciences. 2022 ; 14( 1): 921-929.[citado 2024 out. 31 ] Available from: https://doi.org/10.1515/geo-2022-0406
  • Source: Landslides. 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

      MEENA, Sansar Raj et al. Landslide detection in the Himalayas using machine learning algorithms and U-Net. Landslides, v. 19, p. 1209–1229, 2022Tradução . . Disponível em: https://doi.org/10.1007/s10346-022-01861-3. Acesso em: 31 out. 2024.
    • APA

      Meena, S. R., Soares, L. P., Grohmann, C. H., Van Westen, C., Bhuyan, K., Singh, R. P., et al. (2022). Landslide detection in the Himalayas using machine learning algorithms and U-Net. Landslides, 19, 1209–1229. doi:10.1007/s10346-022-01861-3
    • NLM

      Meena SR, Soares LP, Grohmann CH, Van Westen C, Bhuyan K, Singh RP, Floris M, Catani F. Landslide detection in the Himalayas using machine learning algorithms and U-Net [Internet]. Landslides. 2022 ; 19 1209–1229.[citado 2024 out. 31 ] Available from: https://doi.org/10.1007/s10346-022-01861-3
    • Vancouver

      Meena SR, Soares LP, Grohmann CH, Van Westen C, Bhuyan K, Singh RP, Floris M, Catani F. Landslide detection in the Himalayas using machine learning algorithms and U-Net [Internet]. Landslides. 2022 ; 19 1209–1229.[citado 2024 out. 31 ] Available from: https://doi.org/10.1007/s10346-022-01861-3
  • Source: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. Unidades: IEE, IGC

    Subjects: DESLIZAMENTO DE TERRA, GEOPROCESSAMENTO

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

      XU, Guosen et al. Feature-Based Constraint Deep CNN Method for Mapping Rainfall-Induced Landslides in Remote Regions With Mountainous Terrain: an application to Brazil. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, v. 15, p. 2644-2659, 2022Tradução . . Disponível em: https://doi.org/10.1109/JSTARS.2022.3161383. Acesso em: 31 out. 2024.
    • APA

      Xu, G., Wang, Y., Wang, L., Soares, L. P., & Grohmann, C. H. (2022). Feature-Based Constraint Deep CNN Method for Mapping Rainfall-Induced Landslides in Remote Regions With Mountainous Terrain: an application to Brazil. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 15, 2644-2659. doi:10.1109/JSTARS.2022.3161383.
    • NLM

      Xu G, Wang Y, Wang L, Soares LP, Grohmann CH. Feature-Based Constraint Deep CNN Method for Mapping Rainfall-Induced Landslides in Remote Regions With Mountainous Terrain: an application to Brazil [Internet]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2022 ; 15 2644-2659.[citado 2024 out. 31 ] Available from: https://doi.org/10.1109/JSTARS.2022.3161383.
    • Vancouver

      Xu G, Wang Y, Wang L, Soares LP, Grohmann CH. Feature-Based Constraint Deep CNN Method for Mapping Rainfall-Induced Landslides in Remote Regions With Mountainous Terrain: an application to Brazil [Internet]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2022 ; 15 2644-2659.[citado 2024 out. 31 ] Available from: https://doi.org/10.1109/JSTARS.2022.3161383.
  • Source: Remote Sensing. Unidades: IEE, IGC

    Assunto: DESLIZAMENTO DE TERRA

    Versão PublicadaAcesso à fonteDOIHow to cite
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    • 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: 31 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. 31 ] 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. 31 ] Available from: https://doi.org/10.3390/rs14092237

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