Articles | Volume 23, issue 5
https://doi.org/10.5194/nhess-23-1987-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-23-1987-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Brief communication: Landslide activity on the Argentinian Santa Cruz River mega dam works confirmed by PSI DInSAR
Guillermo Tamburini-Beliveau
CORRESPONDING AUTHOR
Consejo Nacional de Investigaciones Científicas y Técnicas
(CONICET) – Centro de Investigaciones y Transferencia (CIT) de Santa Cruz, Av. Lisandro de la Torre 860, Río Gallegos, Argentina
Sebastián Balbarani
Departamento de Agrimensura, Facultad de Ingeniería, Universidad de Buenos Aires, Ciudad Autónoma de Buenos Aires, Argentina
Facultad de Ingeniería del Ejército, Universidad de la Defensa Nacional, Ciudad Autónoma de Buenos Aires, Argentina
Oriol Monserrat
Geomatics Research Unit, Centre Tecnològic de Telecomunicacions de Catalunya (CTTC/CERCA), Av. Gauss, 7, 08860 Castelldefels (Barcelona), Spain
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Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-230, https://doi.org/10.5194/gmd-2024-230, 2025
Preprint under review for GMD
Short summary
Short summary
This paper presents a new framework for landslide detection using radar and deep learning, informed by data from 73000 landslides across diverse regions in the world. The method showed high accuracy and rapid response potential regardless of weather and illumination conditions. By overcoming the limits of optical satellite imagery, it offers a powerful tool for global landslide detection, benefiting disaster management and advancing methods for monitoring hazardous terrains.
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Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-3-2024, 459–464, https://doi.org/10.5194/isprs-archives-XLVIII-3-2024-459-2024, https://doi.org/10.5194/isprs-archives-XLVIII-3-2024-459-2024, 2024
M. Crosetto, L. Solari, A. Barra, O. Monserrat, M. Cuevas-González, R. Palamà, Y. Wassie, S. Shahbazi, S. M. Mirmazloumi, B. Crippa, and M. Mróz
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2022, 257–262, https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-257-2022, https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-257-2022, 2022
Q. Gao, M. Crosetto, O. Monserrat, R. Palama, and A. Barra
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S. M. Mirmazloumi, Á. F. Gambin, Y. Wassie, A. Barra, R. Palamà, M. Crosetto, O. Monserrat, and B. Crippa
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2022, 307–312, https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-307-2022, https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-307-2022, 2022
J. A. Navarro, D. García, M. Crosetto, and O. Monserrat
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2022, 313–320, https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-313-2022, https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-313-2022, 2022
R. Palamà, M. Crosetto, O. Monserrat, A. Barra, B. Crippa, M. Mróz, N. Kotulak, M. Mleczko, and J. Rapinski
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2022, 321–326, https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-321-2022, https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-321-2022, 2022
Y. Wassie, Q. Gao, O. Monserrat, A. Barra, B. Crippa, and M. Crosetto
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2022, 361–366, https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-361-2022, https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-361-2022, 2022
J. A. Navarro, A. Barra, O. Monserrat, and M. Crosetto
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2021, 163–169, https://doi.org/10.5194/isprs-archives-XLIII-B3-2021-163-2021, https://doi.org/10.5194/isprs-archives-XLIII-B3-2021-163-2021, 2021
Y. Wassie, M. Crosetto, G. Luzi, O. Monserrat, A. Barra, R. Palamá, M. Cuevas-González, S. M. Mirmazloumi, P. Espín-López, and B. Crippa
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2021, 177–182, https://doi.org/10.5194/isprs-archives-XLIII-B3-2021-177-2021, https://doi.org/10.5194/isprs-archives-XLIII-B3-2021-177-2021, 2021
P. Olea, O. Monserrat, C. Sierralta, A. Barra, L. Bono, F. Fuentes, Z. Qiu, and B. Crippa
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3-W12-2020, 1–6, https://doi.org/10.5194/isprs-archives-XLII-3-W12-2020-1-2020, https://doi.org/10.5194/isprs-archives-XLII-3-W12-2020-1-2020, 2020
O. Monserrat, C. Cardenas, P. Olea, V. Krishnakumar, and B. Crippa
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-3-W2-2020, 137–142, https://doi.org/10.5194/isprs-annals-IV-3-W2-2020-137-2020, https://doi.org/10.5194/isprs-annals-IV-3-W2-2020-137-2020, 2020
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Short summary
Landslides and ground deformation associated with the construction of a hydropower mega dam in the Santa Cruz River in Argentine Patagonia have been monitored using radar and optical satellite data, together with the analysis of technical reports. This allowed us to assess the integrity of the construction, providing a new and independent dataset. We have been able to identify ground deformation trends that put the construction works at risk.
Landslides and ground deformation associated with the construction of a hydropower mega dam in...
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