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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

The pandemic emergency caused by the spread of COVID-19 has stressed the importance of promptly identifying new epidemic clusters and patterns, to ensure the implementation of local risk containment measures and provide the needed healthcare to the population. In this framework, artificial intelligence, GIS, geospatial analysis and space assets can play a crucial role. Social media analytics can be used to trigger Earth Observation (EO) satellite acquisitions over potential new areas of human aggregation. Similarly, EO satellites can be used jointly with social media analytics to systematically monitor well-known areas of aggregation (green urban areas, public markets, etc.). The information that can be obtained from the Earth Cognitive System 4 COVID-19 (ECO4CO) are both predictive, aiming to identify possible new clusters of outbreaks, and at the same time supervisorial, by monitoring infrastructures (i.e. traffic jams, parking lots) or specific categories (i.e. teenagers, doctors, teachers, etc.). In this perspective, the technologies described in this paper will allow us to detect critical areas where individuals can be involved in risky aggregation clusters. The ECO4CO data lake will be integrated with ad hoc data obtained by health care structures to understand trends and dynamics, to assess criticalities with respect to medical response and supplies, and to test possibilities useful to tackle potential future emergencies. The System will also provide geographical information on the spread of the infection which will allow an appropriate context-specific public health response to the epidemic. This project has been co-funded by the European Space Agency under its Business Applications programme.

Building footprint detection and outlining from satellite imagery represents a very useful tool in many types of applications, ranging from population mapping to the monitoring of illegal development, from urban expansion monitoring to organizing prompter and more effective rescuer response in the case of catastrophic events. The problem of detecting building footprints in optical, multispectral satellite data is not easy to solve in a general way due to the extreme variability of material, shape, spatial, and spectral patterns that may come with disparate environmental conditions and construction practices rooted in different places across the globe. This difficult problem has been tackled in many different ways since multispectral satellite data at a sufficient spatial resolution started making its appearance on the public scene at the turn of the century. Whereas a typical approach, until recently, hinged on various combinations of spectral–spatial analysis and image processing techniques, in more recent times, the role of machine learning has undergone a progressive expansion. This is also testified by the appearance of online challenges like SpaceNet, which invite scholars to submit their own artificial intelligence (AI)-based, tailored solutions for building footprint detection in satellite data, and automatically compare and rank by accuracy the proposed maps. In this framework, after reviewing the state-of-the-art on this subject, we came to the conclusion that some improvement could be contributed to the so-called U-Net architecture, which has shown to be promising in this respect. In this work, we focused on the architecture of the U-Net to develop a suitable version for this task, capable of competing with the accuracy levels of past SpaceNet competition winners using only one model and one type of data. This achievement could pave the way for achieving better performances than the current state-of-the-art. All these results, indeed, have yet to be augmented through the integration of techniques that in the past have demonstrated a capability of improving the detection accuracy of U-net-based footprint detectors. The most notable cases are represented by an ensemble of different U-Net architectures, the integration of distance transform to improve boundary detection accuracy, and the incorporation of ancillary geospatial data on buildings. Our future work will incorporate those enhancements.

It is obvious that one of the addends is 1 because every number is divisible by 1. Since we are concerned with even perfect numbers, a second addend is 2. It is also evident that every even perfect number must include an even number of odd ad dends. Since one addend is 1, there must be at least one other odd addend. In our exam ple, the second odd addend, 7, is the sum of the preceding addends, when arranged in ascending order. Now, the sum of all proper divisors of the even perfect number, 28, can be expres sed in terms of powers of 2 as follows