Landslide recognition using SVM, Random Forest, and Maximum Likelihood classifiers on high-resolution satellite images: A case study of Itaóca, southeastern Brazil (2021)
- Autores:
- Autores USP: CARVALHO, CARLOS HENRIQUE GROHMANN DE - IEE ; QUINTANILHA, JOSE ALBERTO - IEE ; DIAS, HELEN CRISTINA - IEE ; ALARCON, DIEGO ALEJANDRO SATIZABAL - IGC
- Unidades: IEE; IGC
- Assunto: MUDANÇA CLIMÁTICA
- Agências de fomento:
- Idioma: Inglês
- Imprenta:
- Fonte:
- Título do periódico: Brazilian Journal of Geology
- Volume/Número/Paginação/Ano: v. 51, n.4. p. e20200105/1-10,2021
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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: 18 set. 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 set. 18 ] 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 set. 18 ] Available from: https://www.scielo.br/j/bjgeo/a/Y6s5whm57BV9cgrMDWcJvgp/?format=pdf&lang=en - An object-based approach for semi-automated shallow landslide mapping: suitability and comparison in Itaóca (SP) and Nova Friburgo (RJ), southeastern Brazil
- Statistical-based shallow landslide susceptibility assessment for a tropical environment: a case study in the southeastern Brazilian coast
- Rainfall-Induced Shallow Landslide Recognition and Transferability Using Object-Based Image Analysis in Brazil
- Landslide Susceptibility Mapping in Brazil: a review
- Standards for shallow landslide identification in Brazil: Spatial trends and inventory mapping
- Landslide Segmentation with Deep Learning: evaluating model generalization in rainfall-induced landslides in Brazil
- Distinction between watersheds prone to debris flow, debris flood, and flood using morphometry in Serra do Mar, Brazil (São Paulo State North shore)
- Rainfall-induced debris flows and shallow landslides in Ribeira Valley, Brazil: main characteristics and inventory mapping
- Avaliação das mudanças no armazenamento de água subterrânea na bacia do rio Amazonas a partir do downscaling de dados GRACE/GRACE-FO com modelos Machine Learning
- Application of Object-Based Image Analysis for Detecting and Differentiating between Shallow Landslides and Debris Flows
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