We would like to thank our colleagues for their comment, as we believe that this discussion further highlights the importance of innovative research in the emerging field of InSAR applications to civil engineering structures. We discuss the statement from Lanari et al. (2020): “Our analysis shows that, although both the SBAS and the TomoSAR analyses allow achieving denser coherent pixel maps relevant to the Morandi bridge, nothing of the pre-collapse large displacements reported in Milillo et al. (2019) appears in our results”. In this reply we argue that (1) they cannot detect the pre-collapse movements because they use standard approaches and (2) the signals of interest become observable by changing the point of view.@en

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 led 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.@en

We present a methodology for the assessment of possible pre-failure bridge deformations, based on SyntheticAperture Radar (SAR) observations. We apply this methodology to obtain a detailed 15-year survey of the Morandi bridge (Polcevera Viaduct) in the form of relative displacements across the structure prior to its collapse on August 14th 2018. We generated a displacement map for the structure from space-based SAR measurements acquired by the Italian constellation COSMO-SkyMed and the European constellation Sentinel-1A/B over the period 2009-2018. Historical satellite datasets include Envisat data spanning 2003-2011. The map reveals that the bridge was undergoing an increased magnitude of deformations over time prior to its collapse. This technique shows that the deck next to the collapsed pier was characterized since 2015 by increasing relative displacements. The COSMO-SkyMed dataset reveals the increased deformation magnitude over time of several points located near the strands of this deck between 12th March 2017 and August 2018.@en

We present a methodology for the assessment of possible pre-failure bridge deformations, based on SyntheticAperture Radar (SAR) observations. We apply this methodology to obtain a detailed 15-year survey of the Morandi bridge (Polcevera Viaduct) in the form of relative displacements across the structure prior to its collapse on August 14th 2018. We generated a displacement map for the structure from space-based SAR measurements acquired by the Italian constellation COSMO-SkyMed and the European constellation Sentinel-1A/B over the period 2009-2018. Historical satellite datasets include Envisat data spanning 2003-2011. The map reveals that the bridge was undergoing an increased magnitude of deformations over time prior to its collapse. This technique shows that the deck next to the collapsed pier was characterized since 2015 by increasing relative displacements. The COSMO-SkyMed dataset reveals the increased deformation magnitude over time of several points located near the strands of this deck between 12th March 2017 and August 2018.@en

We present a methodology for the assessment of possible pre-failure bridge deformations, based on SyntheticAperture Radar (SAR) observations. We apply this methodology to obtain a detailed 15-year survey of the Morandi bridge (Polcevera Viaduct) in the form of relative displacements across the structure prior to its collapse on August 14th 2018. We generated a displacement map for the structure from space-based SAR measurements acquired by the Italian constellation COSMO-SkyMed and the European constellation Sentinel-1A/B over the period 2009-2018. Historical satellite datasets include Envisat data spanning 2003-2011. The map reveals that the bridge was undergoing an increased magnitude of deformations over time prior to its collapse. This technique shows that the deck next to the collapsed pier was characterized since 2015 by increasing relative displacements. The COSMO-SkyMed dataset reveals the increased deformation magnitude over time of several points located near the strands of this deck between 12th March 2017 and August 2018.@en

We present a methodology for the assessment of possible pre-failure bridge deformations, based on SyntheticAperture Radar (SAR) observations. We apply this methodology to obtain a detailed 15-year survey of the Morandi bridge (Polcevera Viaduct) in the form of relative displacements across the structure prior to its collapse on August 14th 2018. We generated a displacement map for the structure from space-based SAR measurements acquired by the Italian constellation COSMO-SkyMed and the European constellation Sentinel-1A/B over the period 2009-2018. Historical satellite datasets include Envisat data spanning 2003-2011. The map reveals that the bridge was undergoing an increased magnitude of deformations over time prior to its collapse. This technique shows that the deck next to the collapsed pier was characterized since 2015 by increasing relative displacements. The COSMO-SkyMed dataset reveals the increased deformation magnitude over time of several points located near the strands of this deck between 12th March 2017 and August 2018.@en

The increasing demand for underground infrastructure should be supported by innovation in monitoring and damage assessment solutions to minimise damage to surface structures caused by ground settlements. This paper evaluates the use of multitemporal synthetic aperture radar interferometry (MT-InSAR) to calculate tunnelling-induced deformations of buildings. The paper introduces a step-by-step procedure to use InSAR displacements as an input to the structural damage assessment. After a comparison between traditional and InSAR monitoring data for the London area during the Crossrail excavation, the high resolution, high density InSAR-based displacements were used to evaluate the building deformations for a number of case studies. Results demonstrate the quality of information provided by InSAR data on soil-structure interaction mechanisms. Such information, essential to evaluate current damage assessment procedures, is typically only collected for relatively few buildings due to the cost of traditional monitoring. A comparison between damage indicators derived from greenfield assumptions and building displacements quantifies the practical benefit of the proposed step-by-step procedure. This work aims at filling the gap between the most recent advances in remote sensing and the civil engineering practice, defining the first step of an automated damage assessment procedure which can impact large scale underground projects in urban areas.@en

Structural monitoring of surface building displacements is a significant component of the total financial investment for underground construction projects in urban areas. While traditional monitoring requires in-situ (terrestrial) measurements and trigger levels based on preliminary evaluation of vulnerable structures, very recent advances in Interferometric Synthetic Aperture Radar (InSAR) techniques enable remote monitoring over extensive areas, providing rapid, semi-automatic, and dense measurements with millimetre accuracy. Despite the well-established use of InSAR in geophysical applications, only a few studies are currently available on the use of satellite-based monitoring for the assessment of building deformations and structural damage. The aim of this project is to investigate the potential of InSAR monitoring data as an input to post-tunnelling damage assessment procedures. First, InSAR-based measurements of building displacements, induced by the excavation of Crossrail tunnels in London, were acquired and processed. Then, following the definition of a step-by-step procedure, the satellite-based building displacements were used to evaluate structural deformation parameters typically used in extensive damage assessment procedures. Results show that the number of available measures per single building can enable the estimation of deformation parameters, a capability that is not economically feasible for large scale projects using traditional monitoring systems. The comparison with greenfield predictions offers new insight into the effect of soil-structure interaction and demonstrates the suitability of InSAR monitoring for post-tunnelling damage assessment of structures. The outcome of this work can have a significant economic impact on the construction industry and can advance the knowledge of building and infrastructure response to ground subsidence.@en

Image alignment is without doubt the most crucial step in SAR Interferometry. Interferogram formation requires images to be coregistered with an accuracy of better than 1/8 pixel to avoid significant loss of phase coherence. Conventional interferometric precise coregistration methods for full-resolution SAR data (Single-Look Complex imagery, or SLC) are based on the cross-correlation of the SLC data, either in the original complex form or as squared amplitudes. Offset vectors in slant range and azimuth directions are computed on a large number of windows, according to the estimated correlation peaks. Then, a two-dimensional polynomial of a certain degree is usually chosen as warp function and the polynomial parameters are estimated through LMS fit from the shifts measured on the image windows. In case of rough topography and long baselines, the polynomial approximation for the warp function becomes inaccurate, leading to local misregistrations. Moreover, these effects increase with the spatial resolution and then with the sampling frequency of the sensor, as first results on TerraSAR-X interferometry confirm. An improved, DEM-assisted image coregistration procedure can be adopted for providing higher-order prediction of the offset vectors. Instead of estimating the shifts on a limited number of patches and using a polynomial approximation for the transformation, this approach computes pixel by pixel the correspondence between master and slave by using the orbital data and a reference DEM. This study assesses the performance of this approach with respect to the standard procedure. In particular, both analytical relationships and simulations will evaluate the impact of the finite vertical accuracy of the DEM on the final coregistration precision for different radar postings and relative positions of satellites. The two approaches are compared by processing real data at different carrier frequencies and using the interferometric coherence as quality figure.@en

Image alignment is without doubt the most crucial step in SAR Interferometry. Interferogram formation requires images to be coregistered with an accuracy of better than 1/8 pixel to avoid significant loss of phase coherence. Conventional interferometric precise coregistration methods for full-resolution SAR data (Single-Look Complex imagery, or SLC) are based on the cross-correlation of the SLC data, either in the original complex form or as squared amplitudes. Offset vectors in slant range and azimuth directions are computed on a large number of windows, according to the estimated correlation peaks. Then, a two-dimensional polynomial of a certain degree is usually chosen as warp function and the polynomial parameters are estimated through LMS fit from the shifts measured on the image windows. In case of rough topography and long baselines, the polynomial approximation for the warp function becomes inaccurate, leading to local misregistrations. Moreover, these effects increase with the spatial resolution and then with the sampling frequency of the sensor, as first results on TerraSAR-X interferometry confirm. An improved, DEM-assisted image coregistration procedure can be adopted for providing higher-order prediction of the offset vectors. Instead of estimating the shifts on a limited number of patches and using a polynomial approximation for the transformation, this approach computes pixel by pixel the correspondence between master and slave by using the orbital data and a reference DEM. This study assesses the performance of this approach with respect to the standard procedure. In particular, both analytical relationships and simulations will evaluate the impact of the finite vertical accuracy of the DEM on the final coregistration precision for different radar postings and relative positions of satellites. The two approaches are compared by processing real data at different carrier frequencies and using the interferometric coherence as quality figure.@en

Image alignment is without doubt the most crucial step in SAR Interferometry. Interferogram formation requires images to be coregistered with an accuracy of better than 1/8 pixel to avoid significant loss of phase coherence. Conventional interferometric precise coregistration methods for full-resolution SAR data (Single-Look Complex imagery, or SLC) are based on the cross-correlation of the SLC data, either in the original complex form or as squared amplitudes. Offset vectors in slant range and azimuth directions are computed on a large number of windows, according to the estimated correlation peaks. Then, a two-dimensional polynomial of a certain degree is usually chosen as warp function and the polynomial parameters are estimated through LMS fit from the shifts measured on the image windows. In case of rough topography and long baselines, the polynomial approximation for the warp function becomes inaccurate, leading to local misregistrations. Moreover, these effects increase with the spatial resolution and then with the sampling frequency of the sensor, as first results on TerraSAR-X interferometry confirm. An improved, DEM-assisted image coregistration procedure can be adopted for providing higher-order prediction of the offset vectors. Instead of estimating the shifts on a limited number of patches and using a polynomial approximation for the transformation, this approach computes pixel by pixel the correspondence between master and slave by using the orbital data and a reference DEM. This study assesses the performance of this approach with respect to the standard procedure. In particular, both analytical relationships and simulations will evaluate the impact of the finite vertical accuracy of the DEM on the final coregistration precision for different radar postings and relative positions of satellites. The two approaches are compared by processing real data at different carrier frequencies and using the interferometric coherence as quality figure.@en

Image alignment is without doubt the most crucial step in SAR Interferometry. Interferogram formation requires images to be coregistered with an accuracy of better than 1/8 pixel to avoid significant loss of phase coherence. Conventional interferometric precise coregistration methods for full-resolution SAR data (Single-Look Complex imagery, or SLC) are based on the cross-correlation of the SLC data, either in the original complex form or as squared amplitudes. Offset vectors in slant range and azimuth directions are computed on a large number of windows, according to the estimated correlation peaks. Then, a two-dimensional polynomial of a certain degree is usually chosen as warp function and the polynomial parameters are estimated through LMS fit from the shifts measured on the image windows. In case of rough topography and long baselines, the polynomial approximation for the warp function becomes inaccurate, leading to local misregistrations. Moreover, these effects increase with the spatial resolution and then with the sampling frequency of the sensor, as first results on TerraSAR-X interferometry confirm. An improved, DEM-assisted image coregistration procedure can be adopted for providing higher-order prediction of the offset vectors. Instead of estimating the shifts on a limited number of patches and using a polynomial approximation for the transformation, this approach computes pixel by pixel the correspondence between master and slave by using the orbital data and a reference DEM. This study assesses the performance of this approach with respect to the standard procedure. In particular, both analytical relationships and simulations will evaluate the impact of the finite vertical accuracy of the DEM on the final coregistration precision for different radar postings and relative positions of satellites. The two approaches are compared by processing real data at different carrier frequencies and using the interferometric coherence as quality figure.@en

Image alignment is without doubt the most crucial step in SAR Interferometry. Interferogram formation requires images to be coregistered with an accuracy of better than 1/8 pixel to avoid significant loss of phase coherence. Conventional interferometric precise coregistration methods for full-resolution SAR data (Single-Look Complex imagery, or SLC) are based on the cross-correlation of the SLC data, either in the original complex form or as squared amplitudes. Offset vectors in slant range and azimuth directions are computed on a large number of windows, according to the estimated correlation peaks. Then, a two-dimensional polynomial of a certain degree is usually chosen as warp function and the polynomial parameters are estimated through LMS fit from the shifts measured on the image windows. In case of rough topography and long baselines, the polynomial approximation for the warp function becomes inaccurate, leading to local misregistrations. Moreover, these effects increase with the spatial resolution and then with the sampling frequency of the sensor, as first results on TerraSAR-X interferometry confirm. An improved, DEM-assisted image coregistration procedure can be adopted for providing higher-order prediction of the offset vectors. Instead of estimating the shifts on a limited number of patches and using a polynomial approximation for the transformation, this approach computes pixel by pixel the correspondence between master and slave by using the orbital data and a reference DEM. This study assesses the performance of this approach with respect to the standard procedure. In particular, both analytical relationships and simulations will evaluate the impact of the finite vertical accuracy of the DEM on the final coregistration precision for different radar postings and relative positions of satellites. The two approaches are compared by processing real data at different carrier frequencies and using the interferometric coherence as quality figure.@en