Automated classification of A-DInSAR-based ground deformation by using random forest-Reference-Cited by-同舟云学术

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

Published:2022-10-14 Issue:1 Volume:59 Page:1749-1766
ISSN:1548-1603
Container-title:GIScience & Remote Sensing
language:en
Short-container-title:GIScience & Remote Sensing

Author:

Festa Davide¹,Casagli Nicola¹²,Casu Francesco³,Confuorto Pierluigi¹,De Luca Claudio³,Del Soldato Matteo¹,Lanari Riccardo³,Manunta Michele³,Manzo Mariarosaria³,Raspini Federico¹

Affiliation:

1. Department of Earth Sciences, University of Firenze, Firenze, Italy

2. National Institute of Oceanography and Applied Geophysics - OGS, Sgonico (Trieste), Italy

3. IREA-CNR, Napoli, Italy

Funder

work was supported

Presidency of the Council of Ministers–Department of Civil Protection

Publisher

Informa UK Limited

Subject

General Earth and Planetary Sciences

Link

https://www.tandfonline.com/doi/pdf/10.1080/15481603.2022.2134561

Reference69 articles.

1. Application of Machine Learning to Classification of Volcanic Deformation in Routinely Generated InSAR Data

2. Detecting Ground Deformation in the Built Environment Using Sparse Satellite InSAR Data With a Convolutional Neural Network

3. A-DInSAR Performance for Updating Landslide Inventory in Mountain Areas: An Example from Lombardy Region (Italy)

4. InSAR full-resolution analysis of the 2017–2018 M>6 earthquakes in Mexico

5. Landslide susceptibility mapping using GIS-based weighted linear combination, the case in Tsugawa area of Agano River, Niigata Prefecture, Japan

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