4.7 Article

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

Journal

GISCIENCE & REMOTE SENSING
Volume 59, Issue 1, Pages 1749-1766

Publisher

TAYLOR & FRANCIS LTD
DOI: 10.1080/15481603.2022.2134561

Keywords

Ground deformation; A-DInSAR; machine learning; random forest; automated classification

Funding

  1. Presidenza del Consiglio dei Ministri-Dipartimento della Protezione Civile (Presidency of the Council of Ministers-Department of Civil Protection), through the IREA-CNR/DPC agreement
  2. Presidenza del Consiglio dei Ministri-Dipartimento della Protezione Civile (Presidency of the Council of Ministers-Department of Civil Protection), through CPC-UNIFI agreement
  3. IREA CNR/Italian Ministry of Economic Development DGS-UNMIG agreement
  4. H2020 EPOS-SP [871121]

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By applying machine learning techniques to integrate and analyze large-scale interferometric datasets, it can effectively detect high-displacement areas and classify ground motion sources, showing promising performance especially in northern Italy.
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.

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