We present in this study an enhancement to our previous work, in which an automated method, based on Convolutional Neural Networks (CNNs), has been developed for identifying co-seismic ground deformation patterns within the Differential SAR Interferometry (DInSAR) maps generated through the EPOSAR service of the European Plate Observing System (EPOS) Research Infrastructure. The implemented improvements have been achieved through two main lines of actions. Firstly, the generation of the synthetic dataset, used to train the developed CNN, has been enhanced; this concerns the improvement of the simulation of possible atmospheric disturbance within the DInSAR interferograms, and the introduction of a simulator of phase unwrapping errors. Secondly, the original CNN-based deformation pattern detector layout, performing a binary classification, has been modified to account for a multiclass deformation pattern classification. This allows us extending the capability of the system which, in addition to detect co-seismic deformation patterns, may also provide information on the earthquake source characteristics. The presented solution will be included in the EPOSAR DInSAR processing chain.

Phase unwrapping (PhU) is a challenging step in multi-temporal (MT) Differential SAR Interferometry (DInSAR) techniques that may have a significant impact on the accuracy of the retrieved displacements. In this paper, we present a simple solution to enhance a previously developed Compressive Sensing (CS)-based PhU approach, allowing us to improve its performance in particularly challenging surface displacement scenarios. The proposed method, without altering the technical framework of the CS-based original solution, specifically targets the refinement of the MT interferometric sequence selection process. In particular, starting from the pivotal role of the Delaunay triangulation in the SAR data acquisitions temporal/perpendicular baseline plane to identify the bulk multi-temporal DInSAR interferograms sequence, we easily enrich this sequence by properly adding highly coherent interferograms which have not been initially selected. The overall study is focused on multi-look L-band interferograms of the SAOCOM-1 constellation, relevant to the Stromboli Island (Italy), and a comparative analysis between the results obtained by applying the enhanced method and its original version is presented, showing the improved capability of the former.

We present in this work the results of an extensive, full resolution Parallel SBAS (P-SBAS) processing activity carried out on large multi-temporal SAR data archives of COSMO-SkyMed first (CSK) and second (CSG) generation SLC images, relevant to some of the main cities of the Italian territory, acquired from ascending and descending orbits since 2009. In particular, we first summarize the key points of the exploited, full resolution P-SBAS processing chain. Subsequently, we also describe the main algorithmic developments aimed to exploit large temporal sequences of CSK and CSG SLC data relevant to wide areas. Finally, we present several results highlighting, through some selected examples, that the use of high resolution SAR images, such as those of the CSK-CSG constellation, processed with techniques like the full resolution P-SBAS technique approach, may provide valued-added information that, properly integrated with in-situ measurements, can be very relevant for the assessment of the built-up environment health conditions, at the national scale.

Over the past 30 years it has been widely demonstrated the effectiveness of the Differential Synthetic Aperture Radar Interferometry (DInSAR) technique to retrieve surface deformation information relevant to tectonically active areas. However, this technique exhibits some limitations due the presence of possible decorrelation effects, phase unwrapping errors, and artefacts due to the temporal/spatial variability of the atmospheric conditions between the SAR acquisition pairs exploited to generate the interferograms and the corresponding deformation maps or time series. A challenging situation may arise when an earthquake event occurs and a co-seismic DInSAR deformation map is generated to quickly support risk management operations. In this scenario, having an automatic process reducing the uncertainties of the retrieved, DInSAR-based, surface deformation information could highly improve the quality of the products made available to the scientific community and of the service provided to the national disaster recovery authorities. We present in this work a solution, based on standard CNN architectures embedded in the DInSAR processing chain of the EPOSAR service, developed within the European Plate Observing System (EPOS) Research Infrastructure, to automatically identify co-seismic ground deformation patterns.

We present in this work some advances of the Differential SAR Interferometry (DInSAR) technique referred to as Parallel Small BAseline Subset (P-SBAS) approach, allowing us to effectively process the recently available L-band SAR images acquired by the Argentinian SAOCOM-1 constellation. This activity has been carried out within the DInSAR-3M project, co-funded by the Italian Space Agency, which is aimed to develop technical solutions for the joint exploitation of multi-frequency (X-, C- and L- band) and multi-platform (spaceborne and airborne) DInSAR measurements. The main goal is the generation of surface deformation time series and corresponding mean displacement velocity maps, spatially and temporally dense, for the multi-scale analysis of natural and anthropogenic phenomena.The assessment of the developed advances has been carried out by processing several SAOCOM-1 SAR datasets acquired over Italy between 2019 and 2022.

In this paper, we present an innovative Phase Unwrapping (PhU) procedure for Multi Temporal DInSAR techniques, based on the Extended Minimum Cost Flow technique schema. The developed algorithm benefits from the Compressive Sensing (CS) theory. In particular, the procedure consists of the cascade of three steps. At first, the set of spatially connected arcs between close pixels is identified by selecting the points to be unwrapped and linking them through the Delaunay algorithm; in the second step, the temporal PhU of all the arcs is carried out by using the CS approach and an L1-norm estimator. In the last step, the algorithm uses the unwrapped arcs to perform spatial PhU of each interferogram using MCF technique. To assess the performance of the developed approach, we analyze the area related to Stromboli volcano (Italy) by processing the dataset acquired by Sentinel-1 constellation over descending orbit from 2016 to 2021.

We present in this work a solution to identify Phase Unwrapping (PhU) errors in redundant sequences of small baseline Differential SAR Inteferometry (DInSAR) interferograms like those exploited by the Small Baseline Subset (SBAS) approach, which allows the retrieval of displacement time series. In particular, the temporal information is exploited through the temporal coherence parameter, which represents a point-like quality indicator of the PhU solutions within advanced DInSAR methods like the above mentioned SBAS technique. First of all we show that this parameter needs to be carefully considered because it may lose sensitivity when the number of images of the exploited dataset increases. Subsequently, we explore a different use of the temporal coherence, moving from a single valued parameter to a time-dependent function. This permits to regain sensitivity and to reduce the number of interferograms eligible to be corrected. The last part of the work is devoted to present a case study where the proposed solution allows the identification of PhU errors.

Performance assessment is a pivotal facet within the operational framework of logistics enterprises, functioning as a mechanism to gauge business outcomes and growth potential. For this study, the authors developed a performance evaluation system for logistics enterprises under the paradigm of sustainable development to reveal the fissures and quandaries within the operational milieu by scrutinizing the current state of logistics enterprises. Drawing on pertinent references and empirical inquiries, they employed the entropy weight method to allocate weights to the performance evaluation metrics of logistics enterprises and TOPSIS–grey relational analysis method to comprehensively assess the performance of such enterprises. Empirical findings show that during the period of 2016–2018, a majority of the sampled logistics enterprises demonstrated an ascending trajectory in their comprehensive proximity, and a minority exhibited fluctuating and descending trends. These findings suggest the favorable trajectory of the evolution of logistics enterprises.

In this work, we present an advanced implementation of the full resolution Parallel Small BAseline Subset (P-SBAS) DInSAR processing chain, aimed to effectively and automatically generate DInSAR products (displacement time series and corresponding velocity maps) related to single buildings and infrastructures over the whole Italian territory. The proposed full resolution P-SBAS pipeline exploits innovative hardware and software parallel technologies based on GPUs, which are able to efficiently process large amounts of full resolution DInSAR data stacks in reasonable time frames and with high scalability. The presented results, achieved by processing very large archives of full resolution X-band first and second generation COSMO-SkyMed data and C-band Sentinel-1 images, demonstrate the effectiveness of the proposed solution in terms of computing time and computational efficiency.

In this work, we present a new method based on the compressive sensing (CS) theory to correct phase unwrapping (PhU) errors in the multitemporal sequence of interferograms exploited by advanced differential interferometric synthetic aperture radar (DInSAR) techniques to generate deformation time series. The developed algorithm estimates the PhU errors by using a modified$L_{1}$-norm estimator applied to the interferometric network built in the temporal/spatial baseline plane. Indeed, in order to search the minimum$L_{1}$-norm sparse solution, we apply the iterative reweighted least-squares method with an improved weight function that takes account of the baseline characteristics of the interferometric pairs. Moreover, we also introduce a quality function to identify those solutions that have no physical meaning. Although the proposed approach can be applied to different multitemporal DInSAR approaches, our analysis is tailored to the full-resolution small baseline subset (SBAS) processing chain that we properly modify to implement the proposed CS-based algorithm. To assess the performance of the developed technique, we carry out an extended experimental analysis based on simulated and real SAR data. In particular, we process two wide SAR datasets acquired by Sentinel-1 and COSMO-SkyMed constellations over central Italy between 2011 and 2019. The achieved experimental results clearly demonstrate the effectiveness of the developed approach in retrieving PhU errors and generating displacement time series related to strongly nonlinear deformation phenomena. Indeed, the developed CS-based technique significantly increases the number of detected coherent points and improves the accuracy of the retrieved deformation time series.