87 citations · 97 across the 5 of their papers we have counts for
5 papers
Sparse InSAR Data 3D Inpainting for Ground Deformation Detection Along the Rail Corridor
Odysseas Pappas, Juliet Biggs, David Bull +2
Monitoring of ground movement close to the rail corridor, such as that associated with landslips caused by ground subsidence and/or uplift, is of great interest for the detection a…
Deep Learning Framework for Detecting Ground Deformation in the Built Environment using Satellite InSAR data
Nantheera Anantrasirichai, Juliet Biggs, Krisztina Kelevitz +5
The large volumes of Sentinel-1 data produced over Europe are being used to develop pan-national ground motion services. However, simple analysis techniques like thresholding canno…
The application of Convolutional Neural Networks to Detect Slow, Sustained Deformation in InSAR Timeseries
N. Anantrasirichai, J. Biggs, F. Albino +1
Automated systems for detecting deformation in satellite InSAR imagery could be used to develop a global monitoring system for volcanic and urban environments. Here we explore the…
A deep learning approach to detecting volcano deformation from satellite imagery using synthetic datasets
Nantheera Anantrasirichai, Juliet Biggs, Fabien Albino +1
Satellites enable widespread, regional or global surveillance of volcanoes and can provide the first indication of volcanic unrest or eruption. Here we consider Interferometric Syn…
Detecting Volcano Deformation in InSAR using Deep learning
N. Anantrasirichai, F. Albino, P. Hill +2
Globally 800 million people live within 100 km of a volcano and currently 1500 volcanoes are considered active, but half of these have no ground-based monitoring. Alternatively, sa…