Machine Learning for Defining the Probability of Sentinel-1 Based Deformation Trend Changes Occurrence Article Swipe
Pierluigi Confuorto
,
Camilla Medici
,
Silvia Bianchini
,
Matteo Del Soldato
,
Ascanio Rosi
,
Samuele Segoni
,
Nicola Casagli
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3390/rs14071748
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3390/rs14071748
The continuous monitoring of displacements occurring on the Earth surface by exploiting MTInSAR (Multi Temporal Interferometry SAR) Sentinel-1 data is a solid reality, as testified by the ongoing operational ground motion service in the Tuscany region (Central Italy). In this framework, anomalies of movement, i.e., accelerations or deceleration as seen by the time series of displacement of radar targets, are identified. In this work, a Machine Learning algorithm such as the Random Forest has been used to assess the probability of occurrence of the anomalies induced by slope instability and subsidence. About 20,000 anomalies (about 7000 and 13,000 for the slope instability and the subsidence, respectively) were collected between 2018 and 2020 and were used as input, while ten different variables were selected, five related to the morphological and geological setting of the study area and five to the radar characteristics of the data. The resulting maps may provide useful indications of where a sudden change of displacement trend may occur, analyzing the contribution of each factor. The cross-validation with the anomalies collected in a following timespan (2020–2021) and with official landslide and subsidence inventories provided by the regional authority has confirmed the reliability of the final maps. The adoption of a map for assessing the probability of the occurrence of MTInSAR anomalies may serve as an enhanced geohazard prevention measurement, to be periodically updated and refined in order to have the most precise knowledge possible of the territory.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/rs14071748
- https://www.mdpi.com/2072-4292/14/7/1748/pdf?version=1649220488
- OA Status
- gold
- Cited By
- 13
- References
- 77
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4226326803
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4226326803Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/rs14071748Digital Object Identifier
- Title
-
Machine Learning for Defining the Probability of Sentinel-1 Based Deformation Trend Changes OccurrenceWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-04-05Full publication date if available
- Authors
-
Pierluigi Confuorto, Camilla Medici, Silvia Bianchini, Matteo Del Soldato, Ascanio Rosi, Samuele Segoni, Nicola CasagliList of authors in order
- Landing page
-
https://doi.org/10.3390/rs14071748Publisher landing page
- PDF URL
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https://www.mdpi.com/2072-4292/14/7/1748/pdf?version=1649220488Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://www.mdpi.com/2072-4292/14/7/1748/pdf?version=1649220488Direct OA link when available
- Concepts
-
Geohazard, Geology, Radar, Landslide, Displacement (psychology), Geodesy, Subsidence, Interferometric synthetic aperture radar, Seismology, Time series, Remote sensing, Computer science, Synthetic aperture radar, Geomorphology, Machine learning, Telecommunications, Structural basin, Psychology, PsychotherapistTop concepts (fields/topics) attached by OpenAlex
- Cited by
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13Total citation count in OpenAlex
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2025: 3, 2024: 3, 2023: 4, 2022: 3Per-year citation counts (last 5 years)
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77Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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