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Mapping urban areas from space is a complex task involving the definition of what should be considered as part of an urban agglomerate beyond the built-up features, thus modelling the transition of a city into the surrounding landscape. In this paper, a new technique to map urban areas using multitemporal synthetic aperture radar data is presented. The proposed methodology exploits innovative RGB composites in combination with self-organizing map (SOM) clustering and object-based image analysis. In particular, the clustered product is used to extract a coarse urban area map, which is then refined using object-based processing. In this phase, Delaunay triangulation and the spatial relationship between the identified urban regions are used to model the urban–rural gradient between a city and the surrounding landscape. The technique has been tested in different scenarios representative of structurally different cities in Italy and Germany. The quality of the obtained products is assessed by comparison with the Urban Atlas of the European Environmental Agency, showing good agreement with the adopted reference data despite their different taxonomies. [ABSTRACT FROM AUTHOR]

Automated systems for detecting deformation in satellite interferometric synthetic aperture radar (InSAR) imagery could be used to develop a global monitoring system for volcanic and urban environments. Here, we explore the limits of a convolutional neural networks for detecting slow, sustained deformations in wrapped interferograms. Using synthetic data, we estimate a detection threshold of 3.9 cm for deformation signals alone and 6.3 cm when atmospheric artifacts are considered. Overwrapping reduces this to 1.8 and 5.2 cm, respectively, as more fringes are generated without altering signal to noise ratio. We test the approach on time series of cumulative deformation from Campi Flegrei and Dallol, where overwrapping improves classification performance by up to 15%. We propose a mean‐filtering method for combining results of different wrap parameters to flag deformation. At Campi Flegrei, deformation of 8.5 cm/year was detected after 60 days and at Dallol, deformation of 3.5 cm/year was detected after 310 days. This corresponds to cumulative displacements of 3 and 4 cm consistent with estimates based on synthetic data. Key Points: We adapt a pretrained convolutional neural networks for detecting slow, steady deformation in InSAR imagesSynthetic tests show the detection threshold is 6.3 cm in volcanic environments but is reduced to 5.0 cm by decreasing wrapping intervalWe demonstrate the method on cumulative time series of interferograms from Campi Flegrei, Italy (8.5 cm/yearr) and Dallol, Ethopia (3.5 cm/year) [ABSTRACT FROM AUTHOR]

Structural health monitoring is a crucial issue in areas with different hazard sources, such as Italy. Among non-invasive monitoring techniques, remote sensing provides useful information in supporting the management process and safety evaluations, reducing the impact of disturbances on the functionality of construction systems. The ground displacement time-series based on the analysis of Differential Interferometric Synthetic Aperture Radar (DInSAR) measurements, as well as the information about the geology of the area and the geometry of the construction under monitoring, provides useful data for the built environment's structural assessment. This paper focuses on the structural monitoring and damage assessment of constructions based on the GIS integration of DInSAR measurements, geological investigation, historical surveys and 3D modeling. The methodology is applied to the residential area of Valco San Paolo in the city of Rome (Italy). Once the geological interpretation has confirmed the results of the DInSAR measurements, a quick damage assessment that considers all the possible conditions of the pre-existing damage at the time zero of the monitoring is shown for a damaged manufact in the area. The presented results highlight how the strategy to correlate the DInSAR-monitored ground settlements with the damage scales allows potentially to monitor continuous construction systems. [ABSTRACT FROM AUTHOR]

Pomarico is a very small town in Basilicata, South Italy, historically landslide prone. The latest main landslide started on 25th of January 2019, on the northern east side of the hill, rapidly evolving and causing, on January 29th, the collapse of the main road of the town as well as of the overlooked buildings and serious damages of several buildings of the town. This work introduces the main features of this landslide and the earlier results of the studies on this phenomenon. The main geological characteristics of the site and the morphological features of the landslide are presented. Moreover, the interpretation of the complex failure mechanism as well as an analysis of the singularity of the triggering rainfall event are shown. Finally, this work focuses on the terrestrial radar monitoring system, installed after the event, and its preliminary outcomes. [ABSTRACT FROM AUTHOR]

The construction of tunnels in urban areas can affect the nearby existing infrastructures and buildings, as shallow excavations induce movements up to the ground surface. An important parameter to be monitored during the excavation is the volume loss, which plays a crucial role in determining the ground movements at the surface. InSAR satellite monitoring has the potential to detect ground movements at the millimetric scale on a vast area for tunneling applications. In the present study, the Multi-Temporal InSAR (MT-InSAR) technique, based on the persistent scattering method, is used to retrieve vertical displacements induced by the excavation of twin tunnels of a metro line in the City of Naples (Italy). Here, the volume loss is obtained by fitting a Gaussian curve on the monitored settlement data induced by the excavation of the first tunnel. The latter is then used to predict the settlement of the second excavation about one year later and compared to the MT-InSAR data. These monitored data show the typical shape of the settlement profile, confirming the empirical Gaussian distribution and MT-InSAR capability to detect millimetric displacements. Therefore, MT-InSAR can be used to feed algorithms to improve the prediction of tunneling-induced displacements. [ABSTRACT FROM AUTHOR]

This study presents a comprehensive assessment of a dynamical downscaling of ERA5 Reanalysis recently performed over Italy through the COSMO-CLM model at a convection-permitting scale (0.02°) over the period 1989–2020. Results are analysed against several independent observational datasets and reanalysis products. The capability of the downscaling to realistically represent the climatology for 2 m temperature and precipitation is analysed over the whole peninsula and subdomains. Hourly precipitation patterns, orography effects, and urban climate dynamics are also investigated, highlighting the weaknesses and strengths of the convection-permitting model. In particular, gains in performances are achieved in mountainous areas where the climate characteristics are correctly represented, as are the hourly precipitation characteristics. Losses in performances occur in coastal and flat areas of the Italian peninsula, where the convection-permitting model performance does not seem to be satisfactory, as opposed to complex orographic areas. The adopted urban parameterisation is demonstrated to simulate heat detection for two Italian cities: Rome and Milan. Finally, a subset of extreme climate indicators is evaluated, finding: (i) a region-dependent response, (ii) a notable performance of the convection-permitting model over mountainous areas and (iii) discrepancies in the South, Central and Insular subdomains. Climate indicators detect extreme events at a detailed scale, becoming an important tool for turning climate data into information. [ABSTRACT FROM AUTHOR]

In the last decade, Pride Events across Europe have multiplied, and they are now held in both big cities, as well as small rural towns and villages. This change is particularly interesting considering the predominant 'metronormative' [Halberstam, J. (2005). A queer time and space: Transgender bodies, subcultural lives. New York University Press] character of LGBTQI+ activism that overshadows forms of organisation for LGBTQI+ persons in rural locales and semi-peripheries. Through a theoretical comparison between Pride Events in rural and peripheral localities in both the UK and Italy, this article seeks to understand, why and how Pride Events in rural locales have exponentially multiplied in the last ten years across Europe. To do so, this article looks at the significance of the processes of hyper-localisation and hyper-contextualisation of Pride Events in rural or peripheral settings, focusing on the potential for social and political transformation beyond the dictates of 'metronormative' understandings of queer life in Europe. [ABSTRACT FROM AUTHOR]

This paper proposes a three-phase method that combines multi-source (i.e. topographic, thematic, monitoring) input data in a GIS environment to rank—at small (1:250,000) scale—administrative units (e.g. municipalities) based on their exposure to slow-moving landslide risk within a selected area (e.g. a region) and, accordingly, detect those primarily requiring mitigation measures. The method is applied in the Calabria region (southern Italy) where several municipalities are widely affected by slow-moving landslides that systematically cause damage to buildings and infrastructure networks resulting in significant economic losses. The results obtained are validated based on the information gathered from previous studies carried out at large (municipal) scale. The work undertaken represents a first, fundamental step of a wider circular approach that can profitably facilitate the decision makers in addressing the issue of the slow-moving landslide risk mitigation in a sustainable way. [ABSTRACT FROM AUTHOR]

The reconnaissance, mapping and analysis of kinematic features of slow-moving landslides evolving along medium-deep sliding surfaces in urban areas can be a difficult task due to the presence and interactions of/with anthropic structures/infrastructures and human activities that can conceal morphological signs of landslide activity. The paper presents an integrated approach to investigate the boundaries, type of movement, kinematics and interactions (in terms of damage severity distribution) with the built environment of a roto-translational slow-moving landslide affecting the historic centre of Lungro town (Calabria region, southern Italy). For this purpose, ancillary multi-source data (e.g. geological-geomorphological features and geotechnical properties of geomaterials), both conventional inclinometer monitoring and innovative non-invasive remote sensing (i.e. A-DInSAR) displacement data were jointly analyzed and interpreted to derive the A-DInSAR-geotechnical velocity (DGV) map of the landslide. This result was then cross-compared with detailed information available on the visible effects (i.e. crack pattern and width) on the exposed buildings along with possible conditioning factors to displacement evolution (i.e. remedial works, sub-services, etc.). The full integration of multi-source data available at the slope scale, by maximizing each contribution, provided a comprehensive outline of kinematic-geometric landslide features that were used to investigate the damage distribution and to detect, if any, anomalous locations of damage severity and relative possible causes. This knowledge can be used to manage landslide risk in the short term and, in particular, is propaedeutic to set up an advanced coupled geotechnical-structural model to simulate both the landslide displacements and the behavior of interacting buildings and, therefore, to implement appropriate risk mitigation strategies over medium/long period. [ABSTRACT FROM AUTHOR]

Natural and anthropogenic subsidence such as that in the Como urban area (northern Italy) can cause significant damage to structures and infrastructure, and expose the city's lakefront to an increasing risk of inundation from Lake Como. This phenomenon affecting the Como basin has been studied by several researchers, and the major drivers of subsidence are known. However, the availability of historical InSAR data allowed us to reconsider the relationship between subsidence predisposing factors (i.e., the thicknesses of reworked and compressible layers, overburden stress, and the piezometric level) and ground surface displacements with higher precision over the entire basin. Benefiting from the deep knowledge of the hydromechanical setting of the Como basin and the availability of InSAR measurements from 1992 to 2010, in this paper we model subsidence-related movements using linear and nonlinear regression methods in order to determine the combination of natural and anthropic factors that have caused subsidence in the Como basin over the past decades. The results of this study highlight peculiar patterns of subsidence that suggest the influence of two further causes, namely tectonic control of the sedimentary architecture and diversion of local streams, which have never been considered before. This analysis aims to assess the spatial distribution of subsidence through InSAR analysis in order to enhance the knowledge and understanding of the phenomenon in the Como urban area. The interferometric data could be used to better plan urban risk management strategies. [ABSTRACT FROM AUTHOR]

This work is based on a drainage basin-scale geomorphological investigation combined with flood modeling. It is focused on the assessment of flood critical areas for the implementation of a geomorphology-based urban Early Warning System (EWS) in the urban area of Lanciano and the Feltrino Stream basin (a minor coastal basin of the Abruzzo hills, Central Italy). This area was investigated by combining: pre-existing geological, geomorphological, and hazard data and new detailed field surveys and mapping of geomorphological and hydrographical features (superficial and buried natural and urban stream network). The study was integrated with 2D flood numerical modeling for verifying the expected flooded areas and calibrating the critical areas. All the collected data were integrated into a geodatabase, and an expert-based approach through a geomorphology-based matrix allowed us to define the main categories of flood critical areas. The assessment of the critical areas supported the emplacement of a network of rainfall, temperature, and flood gauges. The geodatabase, the derived critical areas, and the gauge network contributed to set up an urban EWS, integrated with the regional forecast-based warning system. This system provides combined forecast-based, rainfall threshold-based, and flood monitoring-based alerts for floods. It incorporates communication tools for civil protection management. Finally, the EWS provides a tool for civil protection purposes and for the management of flood critical areas and the mitigation of the related risks by local authorities and will be integrated with sensors related to other hazards (i.e., landslides, wind, etc.). [ABSTRACT FROM AUTHOR]

Water depths and velocities predicted inside urban areas during severe storms are traditionally the final result of a chain of hydrologic and hydraulic models. The use of a single model embedding all the components of the rainfall–runoff transformation, including the flux concentration in the river network, can reduce the subjectivity and, as a consequence, the final uncertainty of the computed water depths and velocities. In the model construction, a crucial issue is the management of the topographic data. The information given by a Digital Elevation Model (DEM) available on a regular grid, as well as all the other elevation data provided by single points or contour lines, allow the creation of a Triangulated Irregular Network (TIN) based unstructured digital terrain model, which provides the spatial discretization for both the hydraulic and the hydrologic models. The procedure is split into four steps: (1) correction of the elevation z* measured in the nodes of a preliminary network connecting the edges with all the DEM cell centers; (2) the selection of a suitable hydrographic network where at least one edge of each node has a strictly descending elevation, (3) the generation of the computational mesh, whose edges include all the edges of the hydrographic network and also other lines following internal boundaries provided by roads or other infrastructures, and (4) the estimation of the elevation of the nodes of the computational mesh. A suitable rainfall–runoff transformation model is finally applied to each cell of the identified computational mesh. The proposed methodology is applied to the Sovara stream basin, in central Italy, for two flood events—one is used for parameter calibration and the other one for validation purpose. The comparison between the simulated and the observed flooded areas for the validation flood event shows a good reconstruction of the urban flooding. [ABSTRACT FROM AUTHOR]