AbstractThis paper presents the application of a Structural Health Monitoring (SHM) strategy for bridges affected by slow deformation phenomena to a curved roadway bridge undergoing slow landslide‐induced movements. The chosen multidisciplinary approach is based on the combination of remote sensing data through satellite Synthetic Aperture Radar Interferometry (InSAR) and structural analysis up to collapse investigated through the Applied Element Method (AEM). The results of the application to the considered case study demonstrate that the integration of the displacement information obtained through InSAR technique with the numerical analysis allows for improving the comprehension of the health state of the bridge undergoing slow movements and identifying potentially critical conditions for the structure.

Interferometric Synthetic Aperture Radar satellite measurements are an effective tool for monitoring ground motion with millimetric resolution over long periods of time. The Persistent Scatterer Pair method, developed in [1], is particularly useful for detecting differential displacements of buildings at multiple positions with few assumptions about the background environment. As a result, anomalous behaviours in building motion can be detected through PSP time series, which are commonly used to perform risk assessments in hazardous areas and diagnostic analyses after damage or collapse events. However, current autonomous early warning systems based on PSP-InSAR data are limited to detecting changes in linear trends and rely on sinusoidal and polynomial models [2]. This can be problematic if background signals exhibit more complex behaviours, as anomalous displacements may be difficult to identify. To address this issue, we propose an unsupervised anomaly detection method using Artificial Intelligence algorithms to identify potentially anomalous building motions based on PSP long time-series data.To identify anomalous building motions, we applied two different AI algorithms based on Long Short-Term Memory Autoencoder inspired by [3] and a Graph Neural Network version of it. LSTM Autoencoder is an unsupervised representation learning framework that captures data representations by reconstructing the correct order of shuffled time series. Its encoder part is used to extract feature representations of a time series, while the decoder part is used to reconstruct the time series. By assuming that most stable samples exhibit similar temporal changes, this algorithm can be used for anomaly detection (as the reconstruction loss would be high for anomalous time series).The data used in this study were provided by the European Ground Motion Service over a rectangular area surrounding the city of Rome and includes approximately 500.000 time-series aggregated over more than 80.000 buildings. The time period covered is from 2015 to 2020.In our proposed approach, we first extract deep feature representations for each timestamp of a non-anomalous time series. The feature sequence is then shuffled and passed through an LSTM encoder-decoder network. By learning to reconstruct the feature sequence with the correct order, the network is able to recognize high-level representations of the time series. In the second step, the pre-trained network is used to reconstruct another time series. If the time series is non-anomalous, the correct order can be reconstructed with high confidence; otherwise, it is difficult to reconstruct the correct order. By selecting an appropriate threshold, anomalies can be detected with high reconstruction losses.Overall, our proposed AI-based approach shows promising results for identifying anomalous building motions in PSP long time-series data. The use of unsupervised learning allows for more accurate statistical representations of the data and more reliable detection of anomalous behaviours. This approach has the potential to improve autonomous early warning systems for risk assessments and diagnostic analyses in dangerous areas.This work is part of the RepreSent project funded by the European Space Agency (NO:4000137253/22/I-DT).[1] https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=4779025[2] https://www.mdpi.com/2072-4292/10/11/1816/pdf[3] https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=930722

Oil trajectory analysis (OTA) provides statistics of direction and distance of provenience of oil spills reaching specific coastal sites. Applied to marine protected areas (MPA), this information could be used to introduce priority criteria in satellite oil spill surveillance. OTA in the Mediterranean Sea was based on 10-days oil trajectories tracked backward-in-time for five years (2015–2019) and aggregated on monthly basis. On average, travel time increases from 12 h at 5 km from the coast to 1.5 days at 10 km and 2 days at 15 km. The beaching probability decreases from 25 % at 5 km to 8 % at 10 km and 5 % at 15 km. Locally, the oil transport is influenced by persistent winds and/or energetic current systems in the area. Using an attention threshold of 5 % of beaching probability around MPA, several offshore areas of the Mediterranean Sea deserving high monitoring priority in summer and winter have been identified.

The ILRS contribution to ITRF2020 is a time series of weekly/bi-weekly SINEXs with station position estimates and EOP, from 7-day arcs (1993.0 – 2021.0) and 15-day arcs over 1983.0- 1993.0. Each solution was obtained as the combination of loosely constrained individual solutions from the seven ILRS Analysis Centers: ASI, BKG, DGFI, ESA, GFZ, JCET and NSGF. Everyone followed strict standards agreed within the ILRS Analysis Standing Committee (ASC) and used SLR data from LAGEOS, LAGEOS-2, Etalon-1 and Etalon-2, (LAGEOS-only from 1983 to 1992). The ILRS ASC devised an innovative approach in handling systematic errors in the network, never before utilized. After a 5-year pilot-project documented in Luceri et al., (2019). The Station Systematic Error Monitoring PP (SSEM), delivered a series of long-term mean bias estimates for each station, the time intervals of applicability and their statistics. They were derived from freely adjusted station position and EOP solutions for the period 1993.0 to 2020.5, using the latest satellite CoG model. The simultaneous estimation of the station heights and measurement biases resulted in a self-consistent set of weekly bias estimates for each site. Breaks and “jumps” were used to decne the periods of applicability and to calculate the mean bias and its standard deviation. The mean biases were pre-applied in the re- analysis, limiting the remaining jitter of the bias to negligible level. This approach strengthened the estimation process without a compromise of the cnal results’ accuracy. As a result, the ILRS contribution to ITRF2020 minimized the scale difference between SLR and VLBI to below 2 mm (ITRF2014 ~9 mm). We present an overview of the procedures, models, the improvement over previous ILRS products, focusing especially on the Core ILRS sites, and an overview of how the new model has been implemented in support of the ILRS ogcial products.

Coronavirus disease 2019 demonstrated the inconsistencies in adequately responding to biological threats on a global scale due to a lack of powerful tools for assessing various factors in the formation of the epidemic situation and its forecasting. Decision support systems have a role in overcoming the challenges in health monitoring systems in light of current or future epidemic outbreaks. This paper focuses on some applied examples of logistic planning, a key service of the Earth Cognitive System for Coronavirus Disease 2019 project, here presented, evidencing the added value of artificial intelligence algorithms towards predictive hypotheses in tackling health emergencies. Earth Cognitive System for Coronavirus Disease 2019 is a decision support system designed to support healthcare institutions in monitoring, management and forecasting activities through artificial intelligence, social media analytics, geospatial analysis and satellite imaging. The monitoring, management and prediction of medical equipment logistic needs rely on machine learning to predict the regional risk classification colour codes, the emergency rooms attendances, and the forecast of regional medical supplies, synergically enhancing geospatial and temporal dimensions. The overall performance of the regional risk colour code classifier yielded a high value of the macro-average F1-score (0.82) and an accuracy of 85%. The prediction of the emergency rooms attendances for the Lazio region yielded a very low root mean square error (<11 patients) and a high positive correlation with the actual values for the major hospitals of the Lazio region which admit about 90% of the region's patients. The prediction of the medicinal purchases for the regions of Lazio and Piemonte has yielded a low root mean squared percentage error of 16%. Accurate forecasting of the evolution of new cases and drug utilisation enables the resulting excess demand throughout the supply chain to be managed more effectively. Forecasting during a pandemic becomes essential for effective government decision-making, managing supply chain resources, and for informing tough policy decisions.