- Article
(1811 KB) - Metadata XML
- April 02, 2025
- |
- Home
- |
- Sitemap
- |
- The ISPRS Foundation
- |
- 𝕏
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Articles | Volume XLIII-B3-2022
https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-271-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-271-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
30 May 2022
| 30 May 2022
INFRASTRUCTURE MONITORING USING THE INTERFEROMETRIC SYNTHETIC APERTURE RADAR (INSAR) TECHNIQUE
Q. Gao
Geomatics Research Unit, Centre Tecnològic de Telecomunicacions de Catalunya (CTTC/CERCA), Spain
M. Crosetto
Geomatics Research Unit, Centre Tecnològic de Telecomunicacions de Catalunya (CTTC/CERCA), Spain
O. Monserrat
Geomatics Research Unit, Centre Tecnològic de Telecomunicacions de Catalunya (CTTC/CERCA), Spain
R. Palama
Geomatics Research Unit, Centre Tecnològic de Telecomunicacions de Catalunya (CTTC/CERCA), Spain
Geomatics Research Unit, Centre Tecnològic de Telecomunicacions de Catalunya (CTTC/CERCA), Spain
Related authors
M. Zribi, N. Baghdadi, S. Bousbih, M. El-Hajj, and Q. Gao
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3-W6, 357–361, https://doi.org/10.5194/isprs-archives-XLII-3-W6-357-2019, https://doi.org/10.5194/isprs-archives-XLII-3-W6-357-2019, 2019
Lorenzo Nava, Alessandro Mondini, Kushanav Bhuyan, Chengyong Fang, Oriol Monserrat, Alessandro Novellino, and Filippo Catani
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.
Michele Crosetto, Saeedeh Shahbazi, and Anna Barra
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-3-2024, 77–82, https://doi.org/10.5194/isprs-archives-XLVIII-3-2024-77-2024, https://doi.org/10.5194/isprs-archives-XLVIII-3-2024-77-2024, 2024
Oriol Monserrat, Anna Barra, Marta Béjar-Pizarro, Jonathan S. Rivera, Jorge Pedro Galve, Carolina Guardiola, Maria Cuevas-González, Rosa Maria Mateos, Pablo Ezquerro, Jose Miguel Azañon, Saeedeh Shahbazi, Jose Navarro, Michele Crosetto, and Guido Luzi
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-3-2024, 351–356, https://doi.org/10.5194/isprs-archives-XLVIII-3-2024-351-2024, https://doi.org/10.5194/isprs-archives-XLVIII-3-2024-351-2024, 2024
Maria Carmelia Ramlie, Oriol Monserrat, Bruno Crippa, Paula Olea-Encina, and Michele Crosetto
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, S. Shahbazi, M. Cuevas-González, J. Navarro, and M. Mróz
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-1-W2-2023, 1229–1234, https://doi.org/10.5194/isprs-archives-XLVIII-1-W2-2023-1229-2023, https://doi.org/10.5194/isprs-archives-XLVIII-1-W2-2023-1229-2023, 2023
Guillermo Tamburini-Beliveau, Sebastián Balbarani, and Oriol Monserrat
Nat. Hazards Earth Syst. Sci., 23, 1987–1999, https://doi.org/10.5194/nhess-23-1987-2023, https://doi.org/10.5194/nhess-23-1987-2023, 2023
Short summary
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.
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
N. Kotulak, M. Mleczko, M. Crosetto, R. Palamà, and M. Mróz
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2022, 285–292, https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-285-2022, https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-285-2022, 2022
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
S. Shahbazi, M. Crosetto, and A. Barra
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2022, 349–354, https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-349-2022, https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-349-2022, 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
M. Crosetto, L. Solari, J. Balasis-Levinsen, L. Bateson, N. Casagli, M. Frei, A. Oyen, D. A. Moldestad, and M. Mróz
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2021, 141–146, https://doi.org/10.5194/isprs-archives-XLIII-B3-2021-141-2021, https://doi.org/10.5194/isprs-archives-XLIII-B3-2021-141-2021, 2021
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
M. Crosetto and L. Solari
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-3-W2-2020, 1–6, https://doi.org/10.5194/isprs-annals-IV-3-W2-2020-1-2020, https://doi.org/10.5194/isprs-annals-IV-3-W2-2020-1-2020, 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
J. A. Navarro, G. Luzi, O. Monserrat, and M. Crosetto
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2020, 1685–1690, https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-1685-2020, https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-1685-2020, 2020
M. Crosetto, G. Luzi, O. Monserrat, A. Barra, M. Cuevas-González, R. Palamá, V. Krishnakumar, Y. Wassie, S. M. Mirmazloumi, P. Espín-López, and B. Crippa
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2020, 287–292, https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-287-2020, https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-287-2020, 2020
M. Crosetto, L. Solari, J. Balasis-Levinsen, N. Casagli, M. Frei, A. Oyen, and D. A. Moldestad
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2020, 293–298, https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-293-2020, https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-293-2020, 2020
M. Zribi, N. Baghdadi, S. Bousbih, M. El-Hajj, and Q. Gao
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3-W6, 357–361, https://doi.org/10.5194/isprs-archives-XLII-3-W6-357-2019, https://doi.org/10.5194/isprs-archives-XLII-3-W6-357-2019, 2019
M. Crosetto, O. Monserrat, A. Barra, M. Cuevas-González, V. Krishnakumar, M. Mróz, and B. Crippa
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W13, 1921–1926, https://doi.org/10.5194/isprs-archives-XLII-2-W13-1921-2019, https://doi.org/10.5194/isprs-archives-XLII-2-W13-1921-2019, 2019
M. Crosetto, A. Budillon, A. Johnsy, G. Schirinzi, N. Devanthéry, O. Monserrat, and M. Cuevas-González
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3, 235–238, https://doi.org/10.5194/isprs-archives-XLII-3-235-2018, https://doi.org/10.5194/isprs-archives-XLII-3-235-2018, 2018
V. Krishnakumar, O. Monserrat, M. Crosetto, and B. Crippa
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3, 741–744, https://doi.org/10.5194/isprs-archives-XLII-3-741-2018, https://doi.org/10.5194/isprs-archives-XLII-3-741-2018, 2018
O. Monserrat, A. Barra, G. Herrera, S. Bianchini, C. Lopez, R. Onori, P. Reichenbach, R. Sarro, R. M. Mateos, L. Solari, S. Ligüérzana, and I. P. Carralero
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3-W4, 351–355, https://doi.org/10.5194/isprs-archives-XLII-3-W4-351-2018, https://doi.org/10.5194/isprs-archives-XLII-3-W4-351-2018, 2018
Q. Huang, M. Crosetto, O. Monserrat, and B. Crippa
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2-W4, 457–463, https://doi.org/10.5194/isprs-annals-IV-2-W4-457-2017, https://doi.org/10.5194/isprs-annals-IV-2-W4-457-2017, 2017
M. Crosetto, O. Monserrat, G. Luzi, N. Devanthéry, M. Cuevas-González, and A. Barra
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W7, 593–596, https://doi.org/10.5194/isprs-archives-XLII-2-W7-593-2017, https://doi.org/10.5194/isprs-archives-XLII-2-W7-593-2017, 2017
M. Crosetto, O. Monserrat, N. Devanthéry, M. Cuevas-González, A. Barra, and B. Crippa
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W7, 597–600, https://doi.org/10.5194/isprs-archives-XLII-2-W7-597-2017, https://doi.org/10.5194/isprs-archives-XLII-2-W7-597-2017, 2017
M. Crosetto, O. Monserrat, N. Devanthéry, M. Cuevas-González, A. Barra, and B. Crippa
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B7, 835–839, https://doi.org/10.5194/isprs-archives-XLI-B7-835-2016, https://doi.org/10.5194/isprs-archives-XLI-B7-835-2016, 2016
M. Crosetto, N. Devanthéry, M. Cuevas-González, O. Monserrat, and B. Crippa
Proc. IAHS, 372, 311–314, https://doi.org/10.5194/piahs-372-311-2015, https://doi.org/10.5194/piahs-372-311-2015, 2015
Short summary
Short summary
Persistent Scatterer Interferometry (PSI) is a remote sensing technique used to monitor land deformation from interferometric SAR images. The main products that can be derived using the PSI technique are the deformation maps and the time series of deformation. In this paper, an approach to apply the PSI technique to a stack of Sentinel-1 images is described. Sentinel-1 deformation maps and time series obtained over the metropolitan area of Mexico DF are discussed.
O. Monserrat, J. Moya, G. Luzi, M. Crosetto, J. A. Gili, and J. Corominas
Nat. Hazards Earth Syst. Sci., 13, 1873–1887, https://doi.org/10.5194/nhess-13-1873-2013, https://doi.org/10.5194/nhess-13-1873-2013, 2013
M. Crosetto, J. A. Gili, O. Monserrat, M. Cuevas-González, J. Corominas, and D. Serral
Nat. Hazards Earth Syst. Sci., 13, 923–933, https://doi.org/10.5194/nhess-13-923-2013, https://doi.org/10.5194/nhess-13-923-2013, 2013
The International Society for Photogrammetry and Remote Sensing is a non-governmental organization devoted to the development of international cooperation for the advancement of photogrammetry and remote sensing and their applications. The Society operates without any discrimination on grounds of race, religion, nationality, or political philosophy.
Useful Links
Useful External Links
Our Contact
c/o
Leibniz University Hannover
Institute of Photogrammetry and GeoInformation
Nienburger Str. 1
D-30167 Hannover
GERMANY
Email: isprs-sg@isprs.org