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Automated classification of A-DInSAR-based ground deformation by using random forest

Automated classification of A-DInSAR-based ground deformation by using random forest

Authors :
Davide Festa
Nicola Casagli
Francesco Casu
Pierluigi Confuorto
Claudio De Luca
Matteo Del Soldato
Riccardo Lanari
Michele Manunta
Mariarosaria Manzo
Federico Raspini
Source :
GIScience & Remote Sensing, Vol 59, Iss 1, Pp 1749-1766 (2022)
Publication Year :
2022
Publisher :
Taylor & Francis Group, 2022.

Abstract

Wide-area ground motion monitoring is nowadays achievable via advanced Differential Interferometry SAR (A-DInSAR) techniques which benefit from the availability of large sets of Copernicus Sentinel-1 images. However, it is of primary importance to implement automated solutions aimed at performing integrated analysis of large amounts of interferometric data. To effectively detect high-displacement areas and classify ground motion sources, here we explore the feasibility of a machine learning-based approach. This is achieved by applying the random forest (RF) technique to large-scale deformation maps spanning 2015–2018. Focusing on the northern part of Italy, we train the model to identify landslide, subsidence, and mining-related ground motion with which to construct a balanced training dataset. The presence of noisy signals and other sources of deformation is also tackled within the model construction. The proposed approach relies on the use of explanatory variables extracted from the A-DInSAR datasets and from freely accessible informative layers such as Digital Elevation Model (DEM), land cover maps, and geohazard inventories. In general, the model performance is very promising as we achieved an overall accuracy of 0.97, a true positive rate of 0.94 and an F1-Score of 0.93. The obtained outcomes demonstrate that such transferable and automated approach may constitute an asset for stakeholders in the framework of geohazards risk management.

Details

Language :
English
ISSN :
15481603 and 19437226
Volume :
59
Issue :
1
Database :
Directory of Open Access Journals
Journal :
GIScience & Remote Sensing
Publication Type :
Academic Journal
Accession number :
edsdoj.25fdda9b732b4a1186c1600d018ec01f
Document Type :
article
Full Text :
https://doi.org/10.1080/15481603.2022.2134561