Landslide Segmentation with Deep Learning: evaluating model generalization in rainfall-induced landslides in Brazil (2022)
- Authors:
- USP affiliated authors: CARVALHO, CARLOS HENRIQUE GROHMANN DE - IEE ; DIAS, HELEN CRISTINA - IEE ; GARCIA, GUILHERME PEREIRA BENTO - IGC
- Unidades: IEE; IGC
- DOI: 10.3390/rs14092237
- Assunto: DESLIZAMENTO DE TERRA
- Agências de fomento:
- Language: Inglês
- Imprenta:
- Source:
- Título: Remote Sensing
- Volume/Número/Paginação/Ano: v. 14, n.9,p.e2237/1-17, 2022
- Este periódico é de acesso aberto
- Este artigo NÃO é de acesso aberto
-
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: 11 fev. 2026. -
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 2026 fev. 11 ] 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 2026 fev. 11 ] Available from: https://doi.org/10.3390/rs14092237 - Standards for shallow landslide identification in Brazil: Spatial trends and inventory mapping
- Landslide Susceptibility Mapping in Brazil: a review
- Rainfall-Induced Shallow Landslide Recognition and Transferability Using Object-Based Image Analysis in Brazil
- 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
- Remotely piloted aircraft‐based automated vertical surface survey
- Using terrestrial laser scanner and RPA-based-photogrammetry for surface analysis of a landslide: a comparison
- Examining the Influence of Different Inventories on Shallow Landslide Susceptibility Modeling: an assessment using machine learning and statistical approaches
- Application of Object-Based Image Analysis for Detecting and Differentiating between Shallow Landslides and Debris Flows
- Rainfall-induced debris flows and shallow landslides in Ribeira Valley, Brazil: main characteristics and inventory mapping
Informações sobre o DOI: 10.3390/rs14092237 (Fonte: oaDOI API)
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