High Risk of Secondary Infections Following Thrombotic Complications in Patients With COVID-19

Background. This study’s primary aim was to evaluate the impact of thrombotic complications on the development of secondary infections. The secondary aim was to compare the etiology of secondary infections in patients with and without thrombotic complications. Methods. This was a cohort study (NCT04318366) of coronavirus disease 2019 (COVID-19) patients hospitalized at IRCCS San Raffaele Hospital between February 25 and June 30, 2020. Incidence rates (IRs) were calculated by univariable Poisson regression as the number of cases per 1000 person-days of follow-up (PDFU) with 95% confidence intervals. The cumulative incidence functions of secondary infections according to thrombotic complications were compared with Gray’s method accounting for competing risk of death. A multivariable Fine-Gray model was applied to assess factors associated with risk of secondary infections. Results. Overall, 109/904 patients had 176 secondary infections (IR, 10.0; 95% CI, 8.8–11.5; per 1000-PDFU). The IRs of secondary infections among patients with or without thrombotic complications were 15.0 (95% CI, 10.7–21.0) and 9.3 (95% CI, 7.9–11.0) per 1000-PDFU, respectively (P = .017). At multivariable analysis, thrombotic complications were associated with the development of secondary infections (subdistribution hazard ratio, 1.788; 95% CI, 1.018–3.140; P = .043). The etiology of secondary infections was similar in patients with and without thrombotic complications. Conclusions. In patients with COVID-19, thrombotic complications were associated with a high risk of secondary infections

Change detection in urban areas: Spatial and temporal scales

Urban areas are a challenging environment because of their ever changing structure and the different temporal behaviors and spatial patterns. In this chapter a detailed analysis of some of the questions arising from the use of remotely sensed data in urban area for change detection are addressed. Specifically, the role of very high resolution sensors and their relevance with respect to either fast or slow changes in human settlement is analyzed, with specific stress on rapid mapping in specific sites (hotspots), e.g. for post-disaster damage assessment. Similarly, the possibility to exploit long temporal sequences of coarser resolution data is also explored and discussed, since the availability of huge archives is nowadays a reality that may be used to look for interesting interrelationships between urban area pattern changes and environmental changes, at both the local (town), regional and global level. Examples related to a so-called “hypertemporal” sequences of EO data are offered, and show the great potentials of these data sets. © Springer International Publishing AG 2016

Automated Detection of Changes in Built-Up Areas for Map Updating: A Case Study in Northern Italy

Keeping track of changes in urban areas on a large scale may be challenging due to fragmentation of information. Even more so when changes are unrecorded and sparse across a region, like in the case of long-disused production sites that may be engulfed in vegetation or partly collapse when no-one is witnessing. In Belgium the Walloon Region is leveraging Earth observation satellites to constantly monitor more than 2200 redevelopment sites. Changes are automatically detected by jointly analysing time series of Sentinel-1 and Sentinel-2 acquisitions with a technique developed on Copernicus data, based on ad-hoc filtering of temporal series of both multi-spectral and radar data. Despite different sampling times, availability (due to cloud cover, for multispectral data) and data parameters (incidence angle, for radar data), the algorithm performs well in detecting changes. In this work, we assess how such technique, developed on a Belgian context, with its own construction practices, urban patterns, and atmospheric characteristics, is effectively reusable in a different context, in Northern Italy, where we studied the case of Pavia

Extensive Exposure Mapping in Urban Areas through Deep Analysis of Street-Level Pictures for Floor Count Determination

In order for a risk assessment to deliver sensible results, exposure in the concerned area must be known or at least estimated in a reliable manner. Exposure estimation, though, may be tricky, especially in urban areas, where large-scale surveying is generally expensive and impractical; yet, it is in urban areas that most assets are at stake when a disaster strikes. Authoritative sources such as cadastral data and business records may not be readily accessible to private stakeholders such as insurance companies; airborne and especially satellite-based Earth-Observation data obviously cannot retrieve all relevant pieces of information. Recently, a growing interest is recorded in the exploitation of street-level pictures, procured either through crowdsourcing or through specialized services like Google Street View. Pictures of building facades convey a great amount of information, but their interpretation is complex. Recently, however, smarter image analysis methods based on deep learning started appearing in literature, made possible by the increasing availability of computational power. In this paper, we leverage such methods to design a system for large-scale, systematic scanning of street-level pictures intended to map floor numbers in urban buildings. Although quite simple, this piece of information is a relevant exposure proxy in risk assessment. In the proposed system, a series of georeferenced images are automatically retrieved from the repository where they sit. A tailored deep learning net is first trained on sample images tagged through visual interpretation, and then systematically applied to the entire retrieved dataset. A specific algorithm allows attaching “number of floors” tags to the correct building in a dedicated GIS (Geographic Information System) layer, which is finally output by the system as an “exposure proxy” layer

A Case Study of Rice Paddy Field Detection Using Sentinel-1 Time Series in Northern Italy

Whereas a vast literature exists reporting on mapping of rice paddy fields in Asia based on spaceborne data, especially from radar sensors, comparatively little has been done so far on the European context, where production is much smaller in absolute terms. From a scientific standpoint, it would be interesting to characterize rice paddy fields in terms of typical annual trend of radar response in a context where seasons follow different patterns with respect to the Asian one. In this manuscript we report a case study on a designated set of rice paddy fields in northern Italy, where the largest fraction of European rice paddy fields are located. Building on previous work, more in-depth analysis of the time trends of radar response is carried out, and some preliminary conclusions on features usable in mapping are presented

Mapping European Rice Paddy Fields Using Yearly Sequences of Spaceborne Radar Reflectivity: A Case Study in Italy

Although a vast literature exists on satellite-based mapping of rice paddy fields in Asia, where most of the global production takes place, little has been produced so far that focuses on the European context. Detection and mapping methods that work well in the Asian context will not offer the same performance in Europe, where different seasonal cycles, environmental contexts, and rice varieties make distinctive features dissimilar to the Asian case. In this context, water management is a key clue; watering practices are distinctive for rice with respect to other crops, and within rice there exist diverse cultivation practices including organic and non-organic approaches. In this paper, we focus on satellite-observed water management to identify rice paddy fields cultivated with a traditional agricultural approach. Building on established research results, and guided by the output of experiments on real-world cases, a new method for analyzing time-series of Sentinel-1 data has been developed, which can identify traditional rice fields with a high degree of reliability. Typical watering practices for traditional rice cultivation leave distinctive marks on the yearly sequence of spaceborne radar reflectivity that are identified by the proposed classifier. The method is tested on a small sample of rice paddy fields, built by direct collection of ground reference information. Automated setting of parameters was sufficient to achieve accuracy values beyond 90%, and scanning of a range of values led to touch full score on an independent test set. This work is a part of a broader initiative to build space-based tools for collecting additional pieces of evidence to support food chain traceability; the whole system will consider various parameters, whose analysis procedures are still at their early stages of development

Remote Sensing Applications in Detecting Electromagnetic Earthquake Precursors

This study is intended to highlight an emerging trend in the research field of seismic hazard identification. Among natural disasters, earthquakes have attracted special consideration for their enormous capability to kill, to damage, and to trigger other disasters (tsunami, landslides, technical failures...). Traditionally there has been a focus on quake's rock and soil mechanics to develop a mechanical precursor system for earthquakes. But the instantaneous nature of the hazard so far prevented any breakthrough in the short term prediction of quakes. This fact leads the scientists to rely on the historical data records of certain area to build a seismological probabilistic model. The shortfalls of such approach are widely discussed in the literature, for instance its assumption of the reoccurrence of the historical events with the same frequency. Thus it is widely admitted that our inadequate knowledge regarding a complex physical phenomenon like earthquake is far from allowing us to predict its existence in the short term. Recently there has been a growing trend towards researches discussing the availability of a unified approach towards studying earthquake precursors including the so-called "electromagnetic precursors" as possible clues. The underlying principle is that strain accumulation generates electromagnetic effects through various mechanisms. But, so far, identifying these noisy perturbations and refining them continued to be a lost challenge for scientists due to several complexities including quake nature, measurements sensitivity, and data refinement. These complications always question the reliability all over the prediction process. However, the usage of remote sensing satellites, with the appropriate temporal monitoring, for detecting the infrared thermal anomalies and relating it to quakes has recently reactivated the researches in quakes prediction field [1,2]. Particularly, after the new promising physical explanations offered by the p-hole theory which contributed in adding a new piece to the puzzle for detecting the pre-earthquake electromagnetic precursors[3]. References 1. Choudhury S., Dasgupta S., Saraf A. K., Panda S. (2006). Remote sensing observations of pre-earthquake. International Journal of Remote Sensing, Vol. 27 (20), Pages 4381-4396. 2. Ma Y. , Wu L., Liu S., Ma B. (2011). A new method to extract and analyse abnormal phenomenon of earthquake from remote sensing information. Geoscience and Remote Sensing Symposium (IGARSS), 2011 IEEE International, Pages 2208-2211. 3. Freund F. (2003). Rocks That Crackle and Sparkle and Glow: Strange Pre-Earthquake Phenomena. Journal of ScientiZ c Exploration, Vol. 17, No. 1, pp. 37-71

ASSESSING EARTHQUAKE DAMAGE FROM POST-EVENT VHR RADAR IMAGES ONLY: A PRELIMINARY STUDY ON COSMO/SKYMED IMAGES FROM THE L’AQUILA, 6TH APRIL 2009 EARTHQUAKE

EO-based earthquake damage assessment is a valuable tool to support management of the post-earthquake phase. Most methods available so far either rely on visual interpretation or pre-post-event comparison. Pre-event data may not be available when Very High Resolution spaceborne data is used. In this paper we present some basic facts that may eventually lead to a damage assessment method requiring only post-event data

Insights on Earth Observation Capabilities in Updating the Spatial Distribution of Exposed Values in Quake-Prone Areas

The paper highlight the usage of space borne products to address reliable indicators and aggregation methodologies for tracking the dynamic physical exposure of urban areas to natural disasters. The active view of cities due to the population growth and the increasing urbanization trends lead to a general risk underestimation. Such a result is particularly observed where urban planning is not considered a priority or in areas of non-frequent hazards like earthquakes. The regular consequence of such situation would be people indifference to inhibit hazard-prone areas. The critical point of creating a guided development is by instantaneously monitoring and controlling it. Mainly the sources for such process would be either the slow statistical records (Census) or the separate studies that might lack the statistical significance due to several reasons like their restricted areas of coverage, the limited number of specimens or for being a temporal snap shot of the current situation. In addition to that, the problem gets more complicated when we want to consider areas with access difficulty or data scarcity. The on-going developments of space borne technology have created an expanding hole in the wall of time barrier and though enabled getting the necessary geo-information near the real time. The different capabilities of the sensors used would allow extracting reliable physical indicators of a wide urban area within relatively short time. The reliable extracted indicators like building size, height, occupancy, location, and the usage class‎, when aggregated using a convenient methodology, would enrich the knowledge of the spatial and temporal variation of the physical exposure ‎and thus increase our precision in developing new generation of risk assessment models. Moreover, the foreseen limitation of such monitoring technology like the low accuracy could be counteracted by a convenient integration with ancillary data sources to create a more consistent model. All the above issues are addressed in projects like GEM-IDCT and the FP7 Space SENSUM Project, which will be discussed in the paper