Factors determining subsidence in urbanized floodplains: evidence from MT‐InSAR in Seville (southern Spain)

Major rivers have traditionally been linked with important human settlements throughout history. The growth of cities over recent river deposits makes necessary the use of multidisciplinary approaches to characterize the evolution of drainage networks in urbanized areas. Since under‐consolidated fluvial sediments are especially sensitive to compaction, their spatial distribution, thickness, and mechanical behavior must be studied. Here, we report on subsidence in the city of Seville (Southern Spain) between 2003 and 2010, through the analysis of the results obtained with the Multi‐Temporal InSAR (MT‐InSAR) technique. In addition, the temporal evolution of the subsidence is correlated with the rainfall, the river water column and the piezometric level. Finally, we characterize the geotechnical parameters of the fluvial sediments and calculate the theoretical settlement in the most representative sectors. Deformation maps clearly indicate that the spatial extent of subsidence is controlled by the distribution of under‐consolidated fine‐grained fluvial sediments at heights comprised in the range of river level variation. This is clearly evident at the western margin of the river and the surroundings of its tributaries, and differs from rainfall results as consequence of the anthropic regulation of the river. On the other hand, this influence is not detected at the eastern margin due to the shallow presence of coarse‐grain consolidated sediments of different terrace levels. The derived results prove valuable for implementing urban planning strategies, and the InSAR technique can therefore be considered as a complementary tool to help unravel the subsidence tendency of cities located over under‐consolidated fluvial deposits. Copyright © 2017 John Wiley & Sons, Ltd.Departamento de Geodinámica, Universidad de Granada, EspañaDepartamento de Ingeniería Cartográfica, Geodésica y Fotogrametría, Universidad de Jaén, EspañaCentro de Estudios Avanzados en Ciencias de la Tierra (CEACTierra), Universidad de Jaén, EspañaInstituto Andaluz de Ciencias de la Tierra, Consejo Superior de Investigaciones Científicas, EspañaInstituto Andaluz de Ciencias de la Tierra, Universidad de Granada, EspañaDepartamento de Ingeniería Civil, Universidad de Granada, EspañaInstitute for Systems and Computer Engineering, Technology and Science, Universidade de Trás‐os‐Montes e Alto Douro, PortugalInstituto Geológico y Minero de España, EspañaDepartment of Radar Technology, Netherlands Organisation for Applied Scientific Research, Países BajosGrupo de Investigación Microgeodesia Jaén, Universidad de Jaén, EspañaDepartment of Geoscience and Remote Sensing, Delft University of Technology, Países Bajo

Role of age and comorbidities in mortality of patients with infective endocarditis

[Purpose]: The aim of this study was to analyse the characteristics of patients with IE in three groups of age and to assess the ability of age and the Charlson Comorbidity Index (CCI) to predict mortality. [Methods]: Prospective cohort study of all patients with IE included in the GAMES Spanish database between 2008 and 2015.Patients were stratified into three age groups:<65 years,65 to 80 years,and ≥ 80 years.The area under the receiver-operating characteristic (AUROC) curve was calculated to quantify the diagnostic accuracy of the CCI to predict mortality risk. [Results]: A total of 3120 patients with IE (1327 < 65 years;1291 65-80 years;502 ≥ 80 years) were enrolled.Fever and heart failure were the most common presentations of IE, with no differences among age groups.Patients ≥80 years who underwent surgery were significantly lower compared with other age groups (14.3%,65 years; 20.5%,65-79 years; 31.3%,≥80 years). In-hospital mortality was lower in the <65-year group (20.3%,<65 years;30.1%,65-79 years;34.7%,≥80 years;p < 0.001) as well as 1-year mortality (3.2%, <65 years; 5.5%, 65-80 years;7.6%,≥80 years; p = 0.003).Independent predictors of mortality were age ≥ 80 years (hazard ratio [HR]:2.78;95% confidence interval [CI]:2.32–3.34), CCI ≥ 3 (HR:1.62; 95% CI:1.39–1.88),and non-performed surgery (HR:1.64;95% CI:11.16–1.58).When the three age groups were compared,the AUROC curve for CCI was significantly larger for patients aged <65 years(p < 0.001) for both in-hospital and 1-year mortality. [Conclusion]: There were no differences in the clinical presentation of IE between the groups. Age ≥ 80 years, high comorbidity (measured by CCI),and non-performance of surgery were independent predictors of mortality in patients with IE.CCI could help to identify those patients with IE and surgical indication who present a lower risk of in-hospital and 1-year mortality after surgery, especially in the <65-year group

Few‐shot learning for satellite characterisation from synthetic inverse synthetic aperture radar images

Abstract Space situational awareness systems primarily focus on detecting and tracking space objects, providing crucial positional data. However, understanding the complex space domain requires characterising satellites, often involving estimation of bus and solar panel sizes. While inverse synthetic aperture radar allows satellite visualisation, developing deep learning models for substructure segmentation in inverse synthetic aperture radar images is challenging due to the high costs and hardware requirements. The authors present a framework addressing the scarcity of inverse synthetic aperture radar data through synthetic training data. The authors approach utilises a few‐shot domain adaptation technique, leveraging thousands of rapidly simulated low‐fidelity inverse synthetic aperture radar images and a small set of inverse synthetic aperture radar images from the target domain. The authors validate their framework by simulating a real‐case scenario, fine‐tuning a deep learning‐based segmentation model using four inverse synthetic aperture radar images generated through the backprojection algorithm from simulated raw radar data (simulated at the analogue‐to‐digital converter level) as the target domain. The authors results demonstrate the effectiveness of the proposed framework, significantly improving inverse synthetic aperture radar image segmentation across diverse domains. This enhancement enables accurate characterisation of satellite bus and solar panel sizes as well as their orientation, even when the images are sourced from different domains

Enabling the Processing of Sentinel-1 TOPS Data with the Open-Source DORIS Software (PPT)

Part of Session 3 - Sentinel-1 TOPS InSAR result

Enabling the Processing of Sentinel-1 TOPS Data with the Open-Source DORIS Software (PPT)

Part of Session 3 - Sentinel-1 TOPS InSAR resultsMathematical Geodesy and Positionin

An inventory of Land Subsidence along the southern coast of Spain detected by satellite radar interferometry

International Workshop on “Advances in the Science and Applications of SAR Interferometry and Sentinel-1 InSAR (10º. 2017. Helsinki)Multi-temporal InSAR methods are effective tools for monitoring and investigating surface displacement on Earth based on conventional radar interferometry. These techniques allow us to measure deformation with uncertainties of one millimeter per year, interpreting time series of interferometric phases at coherent point scatterers (PS). Over the last decades, coastal areas in many parts of Spain have undergone a continuous urban expansion because of the growth of cities and development of new residential areas. The transgression of the sea, as a consequence of sea level rise and the subsidence of populated areas, may result in serious problems to many constructions situated in the coastline. This has an important impact on the economy, environment and society, representing a considerable natural hazard. We use ERS-1/2 and Envisat data in the period 1992-2010 to detect subsidence areas over the southern Spanish coast using time series analysis of SAR data.Departamento de Ingeniería Cartográfica, Geodésica y Fotogrametría, Universidad de Jaén, EspañaCentro de Estudios Avanzados en Ciencias de la Tierra, Universidad de Jaén, EspañaGrupo de investigación Microgeodesia Jaén, Universidad de Jaén, EspañaInstituto Geológico y Minero de España, EspañaPeer reviewe