Structural Health Monitoring on Urban Areas by Using Multi Temporal Insar and Deep Learning

The recent advancements in machine learning techniques have opened the door for automatic large scale monitoring of the surface of the earth. For instance, they could be used in order to evaluate and assess civil infrastructures at scale, which is costly due to the fact that typically the existing methods rely on in-situ evaluation. Over the last decade Deep Learning technologies have risen as the state of the art methods for many different machine learning problems due to the fact that they can learn complex features and model complex non-linear behaviours. In this paper we will explore the possibility of using Deep Learning technologies over remote sensing data with the aim of structure health monitoring at scale. We will compare the performance of new Deep Learning technologies with regards to other traditional machine learning methods. For this purpose, we will use InSAR (Interferometry Synthetic Aperture Radar) data which allow us to measure cumulative surface displacement in the line of sight of the sensor with millimetric accuracy. We will analyse multi temporal InSAR data in order to model ground subsidence. In this paper we will discuss how deep learning technologies can learn to detect terrain subsidence over multi-temporal InSAR data automatically, providing much better results than traditional methods.