The increasing availability of Satellite technology for Earth observation enables the monitoring of land subsidence, achieving large-scale and long-term situation awareness for supporting various human activities. Nevertheless, even with the most-recent Interferometric Synthetic Aperture Radar (InSAR) technology, one of the main limitations is signal loss of coherence. This paper introduces a novel method and tool for increasing the spatial density of the surface motion samples. The method is based on Transformers, a machine learning architecture with dominant performance, low calibration cost and agnostic method. This paper covers development and experimentation on four-years surface subsidence (2017-2021) occurring in two Italian regions, Emilia-Romagna and Tuscany, due to ground-water over-pumping using Sentinel-1 data processed with P-SBAS (Parallel Small Baseline Subset) time-series analysis. Experimental results clearly show the potential of the approach. The developed system has been publicly released to guarantee its reproducibility and the scientific collaboration.
Enhancing land subsidence awareness via InSAR data and Deep Transformers
Orlandi D.;Galatolo F. A.;Cimino M. G. C. A.;La Rosa A.;Pagli C.;Perilli N.
2022-01-01
Abstract
The increasing availability of Satellite technology for Earth observation enables the monitoring of land subsidence, achieving large-scale and long-term situation awareness for supporting various human activities. Nevertheless, even with the most-recent Interferometric Synthetic Aperture Radar (InSAR) technology, one of the main limitations is signal loss of coherence. This paper introduces a novel method and tool for increasing the spatial density of the surface motion samples. The method is based on Transformers, a machine learning architecture with dominant performance, low calibration cost and agnostic method. This paper covers development and experimentation on four-years surface subsidence (2017-2021) occurring in two Italian regions, Emilia-Romagna and Tuscany, due to ground-water over-pumping using Sentinel-1 data processed with P-SBAS (Parallel Small Baseline Subset) time-series analysis. Experimental results clearly show the potential of the approach. The developed system has been publicly released to guarantee its reproducibility and the scientific collaboration.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
