Multi-Temporal InSAR Structural Damage Assessment: The London Crossrail Case Study

Spaceborne multi-temporal interferometric synthetic aperture radar (MT-InSAR) is a monitoring technique capable of extracting line of sight (LOS) cumulative surface displacement measurements with millimeter accuracy. Several improvements in the techniques and datasets quality lead to more effective, near real time assessment and response, and a greater ability of constraining dynamically changing physical processes. Using examples of the COSMO-SkyMed (CSK) system, we present a methodology that bridges the gaps between MT-InSAR and the relative stiffness method for tunnel-induced subsidence damage assessment. The results allow quantification of the effect of the building on the settlement profile. As expected the greenfield deformation assessment tends to provide a conservative estimate in the majority of cases (~ 71% of the analyzed buildings), overestimating tensile strains up to 50%. With this work we show how these two techniques in the field of remote sensing and structural engineering can be synergistically used to complement and replace the traditional ground based analysis by providing an extended coverage and a temporally dense set of data

Neural Network Pattern Recognition Experiments Toward a Fully Automatic Detection of Anomalies in InSAR Time Series of Surface Deformation

We present a neural network-based method to detect anomalies in time-dependent surface deformation fields given a set of geodetic images of displacements collected from multiple viewing geometries. The presented methodology is based on a supervised classification approach using combinations of line of sight multitemporal, multi-geometry interferometric synthetic aperture radar (InSAR) time series of displacements. We demonstrate this method with a set of 170 million time series of surface deformation generated for the entire Italian territory and derived from ERS, ENVISAT, and COSMO-SkyMed Synthetic Aperture Radar satellite constellations. We create a training dataset that has been compared with independently validated data and current state-of-the-art classification techniques. Compared to state-of-the-art algorithms, the presented framework provides increased detection accuracy, precision, recall, and reduced processing times for critical infrastructure and landslide monitoring. This study highlights how the proposed approach can accelerate the anomalous points identification step by up to 147 times compared to analytical and other artificial intelligence methods and can be theoretically extended to other geodetic measurements such as GPS, leveling data, or extensometers. Our results indicate that the proposed approach would make the anomaly identification post-processing times negligible when compared to the InSAR time-series processing

Dixon-Souriau equations from a 5-dimensional spinning particle in a Kaluza-Klein framework

The dimensional reduction of Papapetrou equations is performed in a 5-dimensional Kaluza-Klein background and Dixon-Souriau results for the motion of a charged spinning body are obtained. The splitting provides an electric dipole moment, and, for elementary particles, the induced parity and time-reversal violations are explained.Comment: 20 pages, to appear on Physics Letters

Setting up of an experimental site for the continuous monitoring of water discharge, suspended sediment transport and groundwater levels in a mediterranean basin. Results of one year of activity

The study of suspended sediment transport requires continuous measurement of water discharge to better understand the sediment dynamics. Furthermore, a groundwater monitoring network can support the stream discharge measures, as it reveals how the interactions between surface water and groundwater may affect runoff and consequently sediment transport during flood events. An experimental site for the continuous monitoring of water discharge, suspended sediment transport and groundwater levels was set up in the Carapellotto basin (27.17 km2), which is located in Apulia, Southern Italy. Seven flood events that occurred in the operation timespan were covered with a full record of both water discharge and sediment concentration. Some monitoring problems, largely due to the clogging of the float by mud, suggested to improve the experimental set up. The results show high values of suspended sediments concentration which indicate the sub-basin’s key role in the sediment delivery to the whole river system, while counter-clockwise hysteresis loops are the most frequent due to the basin characteristics. The effects of the interaction between surface water and groundwater are related not only to the flood magnitude but also to the hydrogeological features in the hyporheic zone

Extension of Wavenumber Domain Focusing for spotlight COSMO-SkyMed SAR Data

In this work we describe a method to handle curved orbits in wavenumber domain focusing algorithm for high-resolution SAR data acquired by Low Earth Orbit satellites using spotlight mode. The stand..

Bioptic prostatic inflammation correlates with false positive rates of multiparametric magnetic resonance imaging in detecting clinically significant prostate cancer

ntroduction: The aim of this article was to determine the impact of bioptic prostatic inflammation (PI) on the false positive rate of multiparametric magnetic resonance imaging (mp-MRI) in detecting clinically significant prostate ancer (csPCa). Material and methods: Our prostate biopsy database was queried to identify patients who underwent mp-MRI before PB at our institution. A dedicated uropathologist prospectively assessed bioptic PI using the Irani scores. We evaluated the association between mp-MRI findings, bioptic Gleason grade (GG) and aggressiveness of PI, and PCa detection. Results: In total, 366 men were included. In patients with Prostate Imaging Reporting and Data System (PIRADS) 4-5 lesions, the csPCa (GG ≥2) rate was significantly higher in those with low-grade than in those with high-grade PI (36% vs 29.7%; p = 0.002), and in those with low-aggressive than in those with high-aggressive PI (37.7% vs 30.1%; p = 0.0003). The false positive rates of PIRADS 4-5 lesions for any PCa were 34.2% and 57.8% for low- and high-grade PI, respectively (p = 0.002); similarly, they were 29.5% and 59.4% for mildly and highly-aggressive PI (p = 0.0003). Potential study limitations include its retrospective analysis and single-center study and lack of assessment of the type of PI. Conclusions: Bioptic PI directly correlates with false positive rates of mp-MRI in detecting csPCa. Clinicians should be aware that PI remains the most common pitfall of mp-MRI

Combining remote sensing techniques and field surveys for post‑earthquake reconnaissance missions

Remote reconnaissance missions are promising solutions for the assessment of earthquake induced structural damage and cascading geological hazards. Space-borne remote sensing can complement in-field missions when safety and accessibility concerns limit post-earthquake operations on the ground. However, the implementation of remote sensing techniques in post-disaster missions is limited by the lack of methods that combine different techniques and integrate them with field survey data. This paper presents a new approach for rapid post-earthquake building damage assessment and landslide mapping, based on Synthetic Aperture Radar (SAR) data. The proposed texture-based building damage classification approach exploits very high resolution post-earthquake SAR data integrated with building survey data. For landslide mapping, a backscatter intensity-based landslide detection approach, which also includes the separation between landslides and flooded areas, is combined with optical-based manual inventories. The approach was implemented during the joint Structural Extreme Event Reconnaissance, GeoHazards International and Earthquake Engineering Field Investigation Team mission that followed the 2021 Haiti Earthquake and Tropical Cyclone Grace

Detection of New Delhi Metallo-β-Lactamase (Encoded by \u3ci\u3ebla\u3c/i\u3e\u3csub\u3eNDM-1\u3c/sub\u3e) in \u3ci\u3eAcinetobacter schindleri\u3c/i\u3e during Routine Surveillance

A carbapenem-resistant Alcaligenes faecalis strain was isolated from a surveillance swab of a service member injured in Afghanistan. The isolate was positive for blaNDM by real-time PCR. Species identification was reevaluated on three identification systems but was inconclusive. Genome sequencing indicated that the closest relative was Acinetobacter schindleri and that blaNDM-1 was carried on a plasmid that shared \u3e99% identity with one identified in an Acinetobacter lwoffii isolate. The isolate also carried a novel chromosomally encoded class D oxacillinase

Lessons for Remote Post-earthquake Reconnaissance from the 14 August 2021 Haiti Earthquake

On 14th August 2021, a magnitude 7.2 earthquake struck the Tiburon Peninsula in the Caribbean nation of Haiti, approximately 150 km west of the capital Port-au-Prince. Aftershocks up to moment magnitude 5.7 followed and over 1,000 landslides were triggered. These events led to over 2,000 fatalities, 15,000 injuries and more than 137,000 structural failures. The economic impact is of the order of US$1.6 billion. The on-going Covid pandemic and a complex political and security situation in Haiti meant that deploying earthquake engineers from the UK to assess structural damage and identify lessons for future building construction was impractical. Instead, the Earthquake Engineering Field Investigation Team (EEFIT) carried out a hybrid mission, modelled on the previous EEFIT Aegean Mission of 2020. The objectives were: to use open-source information, particularly remote sensing data such as InSAR and Optical/Multispectral imagery, to characterise the earthquake and associated hazards; to understand the observed strong ground motions and compare these to existing seismic codes; to undertake remote structural damage assessments, and to evaluate the applicability of the techniques used for future post-disaster assessments. Remote structural damage assessments were conducted in collaboration with the Structural Extreme Events Reconnaissance (StEER) team, who mobilised a group of local non-experts to rapidly record building damage. The EEFIT team undertook damage assessment for over 2,000 buildings comprising schools, hospitals, churches and housing to investigate the impact of the earthquake on building typologies in Haiti. This paper summarises the mission setup and findings, and discusses the benefits, and difficulties, encountered during this hybrid reconnaissance mission

Combining remote sensing techniques and field surveys for post-earthquake reconnaissance missions

Copyright © The Author(s) 2023. Remote reconnaissance missions are promising solutions for the assessment of earthquake-induced structural damage and cascading geological hazards. Space-borne remote sensing can complement in-field missions when safety and accessibility concerns limit post-earthquake operations on the ground. However, the implementation of remote sensing techniques in post-disaster missions is limited by the lack of methods that combine different techniques and integrate them with field survey data. This paper presents a new approach for rapid post-earthquake building damage assessment and landslide mapping, based on Synthetic Aperture Radar (SAR) data. The proposed texture-based building damage classification approach exploits very high resolution post-earthquake SAR data integrated with building survey data. For landslide mapping, a backscatter intensity-based landslide detection approach, which also includes the separation between landslides and flooded areas, is combined with optical-based manual inventories. The approach was implemented during the joint Structural Extreme Event Reconnaissance, GeoHazards International and Earthquake Engineering Field Investigation Team mission that followed the 2021 Haiti Earthquake and Tropical Cyclone Grace.VM was supported by the Dutch Research Council (NWO), project OCENW.XS5.114. StEER and GHI Data collection was supported by the National Science Foundation (NSF) under Grant CMMI-1841667, the U.S. Geological Survey (USGS) and the U.S. Agency for International Development (USAID), under USGS Cooperative Agreement No. G21AC10343-00 and USAID Award AID-OFDA-T-16-00001, under lead investigator Janise Rodgers