An object-based approach for semi-automated shallow landslide mapping: suitability and comparison in Itaóca (SP) and Nova Friburgo (RJ), southeastern Brazil (2023)
- Authors:
- USP affiliated authors: CARVALHO, CARLOS HENRIQUE GROHMANN DE - IEE ; DIAS, HELEN CRISTINA - IEE
- Unidade: IEE
- DOI: 10.5194/egusphere-egu23-158
- Assunto: GEOMORFOMETRIA
- Language: Inglês
- Imprenta:
- Source:
- Título do periódico: Abstract EGU23
- Conference titles: EGU General Assembly 2023
- Este periódico é de assinatura
- Este artigo NÃO é de acesso aberto
- Cor do Acesso Aberto: closed
-
ABNT
DIAS, Helen Cristina e HOLBLING, Daniel e GROHMANN, Carlos Henrique. An object-based approach for semi-automated shallow landslide mapping: suitability and comparison in Itaóca (SP) and Nova Friburgo (RJ), southeastern Brazil. 2023, Anais.. Viena: Instituto de Energia e Ambiente, Universidade de São Paulo, 2023. Disponível em: https://doi.org/10.5194/egusphere-egu23-158. Acesso em: 30 abr. 2024. -
APA
Dias, H. C., Holbling, D., & Grohmann, C. H. (2023). An object-based approach for semi-automated shallow landslide mapping: suitability and comparison in Itaóca (SP) and Nova Friburgo (RJ), southeastern Brazil. In Abstract EGU23. Viena: Instituto de Energia e Ambiente, Universidade de São Paulo. doi:10.5194/egusphere-egu23-158 -
NLM
Dias HC, Holbling D, Grohmann CH. An object-based approach for semi-automated shallow landslide mapping: suitability and comparison in Itaóca (SP) and Nova Friburgo (RJ), southeastern Brazil [Internet]. Abstract EGU23. 2023 ;[citado 2024 abr. 30 ] Available from: https://doi.org/10.5194/egusphere-egu23-158 -
Vancouver
Dias HC, Holbling D, Grohmann CH. An object-based approach for semi-automated shallow landslide mapping: suitability and comparison in Itaóca (SP) and Nova Friburgo (RJ), southeastern Brazil [Internet]. Abstract EGU23. 2023 ;[citado 2024 abr. 30 ] Available from: https://doi.org/10.5194/egusphere-egu23-158 - Statistical-based shallow landslide susceptibility assessment for a tropical environment: a case study in the southeastern Brazilian coast
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- Landslide Segmentation with Deep Learning: evaluating model generalization in rainfall-induced landslides in Brazil
- Application of Object-Based Image Analysis for Detecting and Differentiating between Shallow Landslides and Debris Flows
- Modelagem da suscetibilidade a escorregamentos rasos com base em análises estatísticas
- Landslide recognition using SVM, Random Forest, and Maximum Likelihood classifiers on high-resolution satellite images: A case study of Itaóca, southeastern Brazil
- Effects of spatial resolution on slope and aspect derivation for regional-scale analysis
- Forest Restoration Monitoring Protocol with a Low-Cost Remotely Piloted Aircraft: lessons Learned from a Case Study in the Brazilian Atlantic Forest
Informações sobre o DOI: 10.5194/egusphere-egu23-158 (Fonte: oaDOI API)
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