ReP USP - Resultado da busca

Artigo de periodico
Monitoring geological risk areas in the city of São Paulo based on multi-temporal high-resolution 3D models (2023)
Sousa, Amanda Mendes de ; Viana, Camila Duelis ; Garcia, Guilherme Pereira Bento ; Grohmann, Carlos Henrique
Source: Remote Sensing. Unidades: IGC, IEE
Subjects: GEOLOGIA AMBIENTAL, MONITORAMENTO AMBIENTAL, DESLIZAMENTO DE TERRA
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
SOUSA, Amanda Mendes de et al. Monitoring geological risk areas in the city of São Paulo based on multi-temporal high-resolution 3D models. Remote Sensing, v. 15, n. 12, p. 3028-, 2023Tradução . . Disponível em: https://doi.org/10.3390/rs15123028. Acesso em: 07 out. 2024.
APA
Sousa, A. M. de, Viana, C. D., Garcia, G. P. B., & Grohmann, C. H. (2023). Monitoring geological risk areas in the city of São Paulo based on multi-temporal high-resolution 3D models. Remote Sensing, 15( 12), 3028-. doi:10.3390/rs15123028
NLM
Sousa AM de, Viana CD, Garcia GPB, Grohmann CH. Monitoring geological risk areas in the city of São Paulo based on multi-temporal high-resolution 3D models [Internet]. Remote Sensing. 2023 ; 15( 12): 3028-.[citado 2024 out. 07 ] Available from: https://doi.org/10.3390/rs15123028
Vancouver
Sousa AM de, Viana CD, Garcia GPB, Grohmann CH. Monitoring geological risk areas in the city of São Paulo based on multi-temporal high-resolution 3D models [Internet]. Remote Sensing. 2023 ; 15( 12): 3028-.[citado 2024 out. 07 ] Available from: https://doi.org/10.3390/rs15123028
Artigo de periodico
Rainfall-Induced Shallow Landslide Recognition and Transferability Using Object-Based Image Analysis in Brazil (2023)
Dias, Helen Cristina ; Holbling,Daniel ; Grohmann, Carlos Henrique
Source: Remote Sensing. Unidade: IEE
Assunto: SENSORIAMENTO REMOTO
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
DIAS, Helen Cristina e HOLBLING, Daniel e GROHMANN, Carlos Henrique. Rainfall-Induced Shallow Landslide Recognition and Transferability Using Object-Based Image Analysis in Brazil. Remote Sensing, v. 15, n. 21, p. art.5137/1-16, 2023Tradução . . Disponível em: https://doi.org/10.3390/rs15215137. Acesso em: 07 out. 2024.
APA
Dias, H. C., Holbling, D., & Grohmann, C. H. (2023). Rainfall-Induced Shallow Landslide Recognition and Transferability Using Object-Based Image Analysis in Brazil. Remote Sensing, 15(21), art.5137/1-16. Recuperado de https://doi.org/10.3390/rs15215137
NLM
Dias HC, Holbling D, Grohmann CH. Rainfall-Induced Shallow Landslide Recognition and Transferability Using Object-Based Image Analysis in Brazil [Internet]. Remote Sensing. 2023 ; 15(21):art.5137/1-16.[citado 2024 out. 07 ] Available from: https://doi.org/10.3390/rs15215137
Vancouver
Dias HC, Holbling D, Grohmann CH. Rainfall-Induced Shallow Landslide Recognition and Transferability Using Object-Based Image Analysis in Brazil [Internet]. Remote Sensing. 2023 ; 15(21):art.5137/1-16.[citado 2024 out. 07 ] Available from: https://doi.org/10.3390/rs15215137
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
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: 07 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. 07 ] 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. 07 ] Available from: https://doi.org/10.3390/rs14092237
Artigo de periodico
Digital Elevation Models: terminology and definitions (2021)
Guth, Peter L ; Van Niekerk, Adriaan ; Grohmann, Carlos Henrique ; Muller, Jan-Peter ; Hawker, Laurence ; Florinsky, Igor ; Gesch, Dean B. ; Reuter, Hannes Isaak ; Herrera-Cruz, Virginia ; Riazanoff, Serge ; López-Vázquez, Carlos ; Carabajal, Claudia ; Albinet, Clement ; Strobl, Peter A
Source: Remote Sensing. Unidade: IEE
Assunto: GEOMORFOMETRIA
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
GUTH, Peter L et al. Digital Elevation Models: terminology and definitions. Remote Sensing, v. 13, n. 18, p. se 2021, 2021Tradução . . Disponível em: https://www.mdpi.com/1263464. Acesso em: 07 out. 2024.
APA
Guth, P. L., Van Niekerk, A., Grohmann, C. H., Muller, J. -P., Hawker, L., Florinsky, I., et al. (2021). Digital Elevation Models: terminology and definitions. Remote Sensing, 13( 18), se 2021. Recuperado de https://www.mdpi.com/1263464
NLM
Guth PL, Van Niekerk A, Grohmann CH, Muller J-P, Hawker L, Florinsky I, Gesch DB, Reuter HI, Herrera-Cruz V, Riazanoff S, López-Vázquez C, Carabajal C, Albinet C, Strobl PA. Digital Elevation Models: terminology and definitions [Internet]. Remote Sensing. 2021 ;13( 18): se 2021.[citado 2024 out. 07 ] Available from: https://www.mdpi.com/1263464
Vancouver
Guth PL, Van Niekerk A, Grohmann CH, Muller J-P, Hawker L, Florinsky I, Gesch DB, Reuter HI, Herrera-Cruz V, Riazanoff S, López-Vázquez C, Carabajal C, Albinet C, Strobl PA. Digital Elevation Models: terminology and definitions [Internet]. Remote Sensing. 2021 ;13( 18): se 2021.[citado 2024 out. 07 ] Available from: https://www.mdpi.com/1263464
Artigo de periodico
Forest Restoration Monitoring Protocol with a Low-Cost Remotely Piloted Aircraft: lessons Learned from a Case Study in the Brazilian Atlantic Forest (2021)
Albuquerque, Rafael Walter ; Ferreira, Manuel Eduardo ; Olsen, Søren Ingvor ; Tymus, Julio Ricardo Caetano ; Balieiro, Cintia Palheta ; Mansur, Hendrik; Moura, Ciro José Ribeiro ; Costa, João Vitor Silva ; Branco,Maurício Ruiz Castello; Grohmann, Carlos Henrique
Source: Remote Sensing. Unidade: IEE
Subjects: MONITORAMENTO AMBIENTAL, SENSORIAMENTO REMOTO
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
ALBUQUERQUE, Rafael Walter et al. Forest Restoration Monitoring Protocol with a Low-Cost Remotely Piloted Aircraft: lessons Learned from a Case Study in the Brazilian Atlantic Forest. Remote Sensing, v. 13, n. 12, p. art.2401/1-21, 2021Tradução . . Disponível em: https://doi.org/10.3390/rs13122401. Acesso em: 07 out. 2024.
APA
Albuquerque, R. W., Ferreira, M. E., Olsen, S. I., Tymus, J. R. C., Balieiro, C. P., Mansur, H., et al. (2021). Forest Restoration Monitoring Protocol with a Low-Cost Remotely Piloted Aircraft: lessons Learned from a Case Study in the Brazilian Atlantic Forest. Remote Sensing, 13( 12), art.2401/1-21. doi:10.3390/rs13122401
NLM
Albuquerque RW, Ferreira ME, Olsen SI, Tymus JRC, Balieiro CP, Mansur H, Moura CJR, Costa JVS, Branco MRC, Grohmann CH. Forest Restoration Monitoring Protocol with a Low-Cost Remotely Piloted Aircraft: lessons Learned from a Case Study in the Brazilian Atlantic Forest [Internet]. Remote Sensing. 2021 ;13( 12): art.2401/1-21.[citado 2024 out. 07 ] Available from: https://doi.org/10.3390/rs13122401
Vancouver
Albuquerque RW, Ferreira ME, Olsen SI, Tymus JRC, Balieiro CP, Mansur H, Moura CJR, Costa JVS, Branco MRC, Grohmann CH. Forest Restoration Monitoring Protocol with a Low-Cost Remotely Piloted Aircraft: lessons Learned from a Case Study in the Brazilian Atlantic Forest [Internet]. Remote Sensing. 2021 ;13( 12): art.2401/1-21.[citado 2024 out. 07 ] Available from: https://doi.org/10.3390/rs13122401

USP Schools

ReP

Filtros

Authors

Non normalized affiliation

Countries of institutions of collaboration