Search Results (15)

This study assesses subsidence-induced risk to urban infrastructure in three major Italian cities—Rome, Bologna, and Florence—by integrating satellite-based persistent scatterer interferometric synthetic aperture radar (PSInSAR) ground displacement data with urban vulnerability metrics into a novel risk assessment workflow, incorporating land use and population data from the Copernicus Land Monitoring Service (CLMS)—Urban Atlas. This analysis exploits ERS-1/2, ENVISAT, and COSMO-SkyMed PSInSAR datasets from the Italian Extraordinary Plan of Environmental Remote Sensing, plus Sentinel-1 datasets from CLMS—European Ground Motion Service (EGMS), and spans a 30-year period, thus capturing both historical and recent subsidence trends. Angular distortion is introduced as a critical parameter for assessing potential structural damage due to differential settlement, which helps to quantify subsidence-induced hazards more precisely. The results reveal variable subsidence hazard patterns across the three cities, with specific areas exhibiting significant differential ground deformation that poses risks to key infrastructure. A total of 36.15, 11.44, and 0.43 km² of land at high to very high risk are identified in Rome, Bologna, and Florence, respectively. By integrating geospatial and vulnerability data at the building-block level, this study offers a more comprehensive understanding of subsidence-induced risk, potentially contributing to improved management and mitigation strategies in urban areas. This study contributes to the limited literature on embedding PSInSAR data into urban risk assessment workflows and provides a replicable framework for future applications in other urban areas. Full article

Current information on urban land use and surface cover is derived from the land classification of cities, facilitating accurate future urban planning. Key insights are driven by multi-year remote sensing data. These data, when analyzed, produce high-resolution changes on the Earth’s surface. In this context, publicly accessible Urban Atlas data are employed for the high-precision and high-resolution classification and monitoring of terrestrial surfaces. These datasets, which are useful for preserving natural resources, guiding spatial developments, and mitigating pollution, are crucial for monitoring changes and managing cities. This research aims to analyze and contrast land use and land cover (LULC) changes in Gaziantep (Turkey) between 2010 and 2018 using Urban Atlas data, and to investigate correlations between the city’s statistical data and LULC changes. Gaziantep’s urban dynamics were analyzed using Urban Atlas datasets from 2010 to 2015 and 2012 to 2018, the latter part of Copernicus, the European Earth Observation Programme. To understand the impact of LULC changes on urban landscapes, people, and the environment, official environmental and demographic statistics spanning four years were sourced and studied. The findings reveal a trend of agricultural and vacant lands evolving into residential and industrial zones, with such changes likely to increase in the near future, given the growth of building zones. While some land classes have shown consistent area values annually, residential and industrial zones have expanded in response to housing and employment demands. The most significant alterations have occurred in the last three years. Shifts in urban configurations align closely with migratory patterns, reflecting notable variations in factors like population, consumption, and pollution. Full article

This research investigates the post-earthquake performances of structures in four rural villages in the Moroccan Atlas, emphasizing common construction system characteristics and design flaws that render buildings susceptible to seismic events. Village selection was based on a prior multispectral satellite-image study, proving effective for planning high-impact, post-earthquake field campaigns. The significance of this research resides in on-site data collection, facilitating the physical assessment of earthquake-induced damage and identification of inherent vulnerabilities in construction systems. The constructions in the study area exhibited structural design deficiencies, inadequate construction techniques, and urban modifiers, leading to damage extensively documented in the literature, as well as less-documented unique damage. Predominant seismic-design shortcomings in the study area included subpar material quality, insufficient earthquake-resistant design, and unskilled labor. In situ data were complemented by a global geospatial approach using differential synthetic aperture radar interferometry with Copernicus Sentinel 1 data. Once calibrated the proposed methodology with field data, the analysis of remote sensing processing results, allow assessing the damages in other earthquake-affected areas, including those not visited in the field but also impacted by the seismic event. Full article

At a time of rising urbanisation and climate change, urban green spaces (UGSs) are an essential element to help adapt to extreme weather events. Especially in urban core areas, heat and drought are regarded as human stress factors. The delineation of such areas constitutes an important reference geometry in topographic geodata (urban mask). This article deals with possibilities for investigating UGSs in European cities—based on unified urban masks—by applying city-wide metrics to Copernicus data (Urban Atlas including the Street Tree Layer). Both public and tree-covered urban green spaces are examined in detail. Selected results are presented for 30 European cities that display a wide range of urban structures. The spatial reference to uniformly delineated urban masks places the analytical focus of city-wide metrics onto corresponding core areas. In general, the values of UGS metrics vary considerably between cities, indicating the strong influence of city-specific factors on urban structures in Europe. For the comparative analysis of tree-covered urban areas, the Urban Green Raster Germany and a municipal tree register are used to provide additional data sources. The regular updating of the Copernicus dataset means that green spaces in European cities can be monitored, also using urban masks. Full article

Meteorological and climate prediction models at the urban scale increasingly require more accurate and high-resolution data. The Local Climate Zone (LCZ) system is an initiative to standardize a classification scheme of the urban landscape, based mainly on the properties of surface structure (e.g., building, tree height, density) and surface cover (pervious vs. impervious). This approach is especially useful for studying the influence of urban morphology and fabric on the surface urban heat island (SUHI) effect and to evaluate how changes in land use and structures affect thermal regulation in the city. This article will demonstrate three different methodologies of creating LCZs: first, the World Urban Database and Access Portal Tools (WUDAPT); second, using Copernicus Urban Atlas (UA) data via a geographic information system (GIS) client directly; and third via Google Earth Engine (GEE) using Oslo, Norway as the case study. The WUDAPT and GEE methods incorporate a machine learning (random forest) procedure using Landsat 8 imagery, and offer the most precision while requiring the most time and familiarity with GIS usage and satellite imagery processing. The WUDAPT method is performed principally using multiple GIS clients and image processing tools. The GEE method is somewhat quicker to perform, with work performed entirely on Google’s sites. The UA or GIS method is performed solely via a GIS client and is a conversion of pre-existing vector data to LCZ classes via scripting. This is the quickest method of the three; however, the reclassification of the vector data determines the accuracy of the LCZs produced. Finally, as an illustration of a practical use of LCZs and to further compare the results of the three methods, we map the distribution of the temperature according to the LCZs of each method, correlating to the land surface temperature (LST) from a Landsat 8 image pertaining to a heat wave episode that occurred in Oslo in 2018. These results show, in addition to a clear LCZ-LST correspondence, that the three methods produce accurate and similar results and are all viable options. Full article

The city of Izmir (Turkey) has experienced one of the most rapid and fastest urbanization processes in the last thirty years; more than 33 thousand hectares of agricultural and seminatural land have been transformed into urban areas, leading to a drastic reduction of biodiversity and hard deployments of the ecosystem service supply. In this perspective, the potential definition of methodologies to design multifunctional green infrastructures is extremely important to challenge the effects of climate change. The aim of this study is to propose an easy and replicable methodology to design a Green Infrastructure at the neighbourhood level in one of the most important districts of Izmir: Çiğli. To this end, we combined historical land-use change analysis (based on Urban Atlas, Copernicus Land Monitoring Service) with environmental and ecosystem mapping in a Geographic Information System environment (ESRI ArcMap 10.8.1) while creating a composite layer based on unweighted overlays of Imperviousness, Tree Cover Density, and Habitat Quality. Results were used to design the Green Infrastructure of Çiğli and suggest context-based strategies for urban adaptation, including Nature-Based Solutions for core, edge, and urban links. Full article

High buildings have generally changed the morphology of cities in recent decades, and they have a significant impact on multiple processes in the urban area. Building height is one of the criteria for urban land cover classification in local climate zone delineation and urban heat island modeling. The European Union’s Earth observation program Copernicus aims to achieve a global, continuous, autonomous, high-quality, wide-range Earth observation capacity. One of the most recent Urban Atlas layers is the Building Height 2012 (BH2012) layer released in 2018, which consists of a 10 m resolution raster layer containing height information generated for core urban areas of the capitals of the EEA38 countries and the United Kingdom. This contribution aims to present the accuracy validation of the BH2012 data in Bratislava using the Slovak Basic Database for the Geographic Information System (ZBGIS). To compare the two datasets, four different tests were performed for the following group of landmark buildings: (i) with area > 100 m², (ii) in Urban Atlas classes with soil sealing > 10%, (iii) with height > 50 m, (iv) with area > 1 ha. The results demonstrate the effect of the building’s area and compactness on the vertical accuracy of the BH2012 Copernicus data. The greater the building’s area and compactness, the smaller the difference between its height value in BH2012 and ZBGIS. The Urban Atlas class 11100 Continuous Urban Fabric (soil sealing: >80%) recorded the lowest vertical accuracy. The BH2012 database provides sufficiently accurate data for primary planning analyses of public administration bodies and various stakeholders who need to obtain information on the nature of a locality for development activities and small-scale environmental analyses. However, for detailed studies focusing on the quality of life in cities at the local level, more precise identification of the building height is recommended. Full article

Ukraine is an associate member of the European Union, and in the coming years, it is expected that all the data and services already used by European Union countries will become available for Ukraine. An important program, which is the basis for building European monitoring services for smart cities, is the Copernicus program. The two most important services of this program are the Copernicus Land Monitoring Service (CLMS) and the Copernicus Atmosphere Monitoring Service (CAMS). CLMS provides important information on land use in Europe. In the context of smart cities, the most valuable tool is the Urban Atlas service, which is related to local CLMS services and provides a detailed digital city plan in vector form, which is segmented into small functional areas classified by Coordinate Information on the Environment (CORINE) nomenclature. The Urban Atlas is a geospatial layer with high resolution, built for all European cities with a population of more than 100,000. It combines high-resolution satellite data, city segmentation by blocks and functional urban areas (FUAs), important city infrastructure, etc. This product is used as a basis for city planning and obtaining analytics on the most important indicators of city development, including air quality monitoring. For Ukraine, such geospatial products are not provided under the Copernicus program. In this article, FUAs are developed for Ukrainian cities using European technology. It is important to start work on this program’s implementation as early as possible so that when the first city atlas appears, Ukraine will be ready to work with it together with the European community. This requires preparing the basis for national research and training national stakeholders and consumers to use this product. To make this happen, it is necessary to have a national geospatial product that can be used as an analogue of the city atlas. In this article, the authors analyzed the existing methods of air quality assessment and the Global Sustainable Development Goal (SDG) indicator 11.6.2, “Annual mean levels of fine particulate matter (e.g., PM_2.5 and PM₁₀) in cities (population weighted)”, achieved for European cities. Based on this, indicator 11.6.2 was then evaluated for the first time in Ukraine, considering the next 5 years. For the correct use of global products for Ukraine, CAMS global satellite data and population data (Global Human Settlement Layer and NASA population data) for Ukrainian cities were validated. These studies showed a statistically significant result and, therefore, demonstrated that global products can be used to monitor air quality both at the city level and for Ukraine as a whole. The obtained results were analyzed, and the values of indicator 11.6.2 for Ukraine were compared with those for other European countries. Full article

As cities are growing in size and complexity, the estimation of air pollution exposure requires a detailed spatial representation of air pollution levels, rather than homogenous fields, provided by global- or regional-scale models. A critical input for city-scale modeling is a timely and spatially resolved emission inventory. Bottom–up approaches to create urban-scale emission inventories can be a demanding and time-consuming task, whereas local emission rates derived from a top–down approach may lack accuracy. In the frame of this study, the UrbEm approach of downscaling gridded emission inventories is developed, investing upon existing, open access, and credible emission data sources. As a proof-of-concept, the regional anthropogenic emissions by Copernicus Atmospheric Monitoring Service (CAMS) are handled with a top–down approach, creating an added-value product of anthropogenic emissions of trace gases and particulate matter for any city (or area) of Europe, at the desired spatial resolution down to 1 km. The disaggregation is based on contemporary proxies for the European area (e.g., Global Human Settlement population data, Urban Atlas 2012, Corine, OpenStreetMap data). The UrbEm approach is realized as a fully automated software tool to produce a detailed mapping of industrial (point), (road-) transport (line), and residential/agricultural/other (area) emission sources. Line sources are of particular value for air quality studies at the urban scale, as they enable explicit treatment of line sources by models capturing among others the street canyon effect and offer an overall better representation of the critical road transport sector. The UrbEm approach is an efficient solution for such studies and constitutes a fully credible option in case high-resolution emission inventories do not exist for a city (or area) of interest. The validity of UrbEm is examined through the evaluation of high-resolution air pollution predictions over Athens and Hamburg against in situ measurements. In addition to a better spatial representation of emission sources and especially hotspots, the air quality modeling results show that UrbEm outputs, when compared to a uniform spatial disaggregation, have an impact on NO₂ predictions up to 70% for urban regions with complex topographies, which corresponds to a big improvement of model accuracy (FAC2 > 0.5), especially at the source-impacted sites. Full article

Globally, processes that drive urbanization have mostly evolved within economic downturns. Economic crises have been more severe and frequent, particularly in the Mediterranean region. However, studies on the recession effects on urbanization are limited. The present study explores possible differences in spatial direction and intensity of land-use change trajectories at two time intervals (2006–2012, 2012–2018) using high-resolution Copernicus Land Urban Atlas images in the Rome metropolitan area. To this aim, a landscape ecology classical approach based on land-use metric analysis combined with a multivariate spatial analysis has been carried out. Results have identified different land-use change patterns during expansion and recession. “Greening”, defined as the conversion of urban marginal areas into croplands and forests, increased during the recession. At the same time, the rate of urban expansion into rural areas decreased, thus indicating a beneficial effect of economic downturns in reducing urban sprawl. Full article

The integration of geography and machine learning can produce novel approaches in addressing a variety of problems occurring in natural and human environments. This article presents an experiment that identifies cities that are similar according to their land use data. The article presents interesting preliminary experiments with screenshots of maps from the Czech map portal. After successfully working with the map samples, the study focuses on identifying cities with similar land use structures. The Copernicus European Urban Atlas 2012 was used as a source dataset (data valid years 2015–2018). The Urban Atlas freely offers land use datasets of nearly 800 functional urban areas in Europe. To search for similar cities, a set of maps detailing land use in European cities was prepared in ArcGIS. A vector of image descriptors for each map was subsequently produced using a pre-trained neural network, known as Painters, in Orange software. As a typical data mining task, the nearest neighbor function analyzes these descriptors according to land use patterns to find look-alike cities. Example city pairs based on land use are also presented in this article. The research question is whether the existing pre-trained neural network outside cartography is applicable for categorization of some thematic maps with data mining tasks such as clustering, similarity, and finding the nearest neighbor. The article’s contribution is a presentation of one possible method to find cities similar to each other according to their land use patterns, structures, and shapes. Some of the findings were surprising, and without machine learning, could not have been evident through human visual investigation alone. Full article

CDR (Call Detail Record) data are one type of mobile phone data collected by operators each time a user initiates/receives a phone call or sends/receives an sms. CDR data are a rich geo-referenced source of user behaviour information. In this work, we perform an analysis of CDR data for the city of Milan that originate from Telecom Italia Big Data Challenge. A set of graphs is generated from aggregated CDR data, where each node represents a centroid of an RBS (Radio Base Station) polygon, and each edge represents aggregated telecom traffic between two RBSs. To explore the community structure, we apply a modularity-based algorithm. Community structure between days is highly dynamic, with variations in number, size and spatial distribution. One general rule observed is that communities formed over the urban core of the city are small in size and prone to dynamic change in spatial distribution, while communities formed in the suburban areas are larger in size and more consistent with respect to their spatial distribution. To evaluate the dynamics of change in community structure between days, we introduced different graph based and spatial community properties which contain latent footprint of human dynamics. We created land use profiles for each RBS polygon based on the Copernicus Land Monitoring Service Urban Atlas data set to quantify the correlation and predictivennes of human dynamics properties based on land use. The results reveal a strong correlation between some properties and land use which motivated us to further explore this topic. The proposed methodology has been implemented in the programming language Scala inside the Apache Spark engine to support the most computationally intensive tasks and in Python using the rich portfolio of data analytics and machine learning libraries for the less demanding tasks. Full article

Human activity combined with the dynamics of severe climate conditions are accepted the main drivers of wildfire events in the Mediterranean region. This fact is urging for further comprehensive research focusing on the wildland-urban interface (WUI) at metropolitan scale, at which the tension between the cause and effect of wildfire is the highest. In this context, the study brings a comparative case between two metropolitan areas from Western Balkan countries, the forest lands of which are classified by their index of wildfire ignition probability (WIPI) and wildfire spreading capacity (WSCI). Originally, both indexing methods rely on a multi-criteria evaluation which considers simultaneously the geophysical, hydrometeorological and anthropogenic factors of the territory. All stages of the process are performed by utilizing QGIS software. First, the forest surfaces within the metropolitan zone of Tirana (AL) and Sarajevo (BH) are extracted from Urban Atlas land cover data being provided as an open source by Copernicus data portal (EU). Reference points grid (distance of 100m) overlapping with the forest surfaces serve as pivot points to which the relative values of each criteria are projected. Later the absolute values are normalized into 10 classes via Jenks natural break method. The class value of each criterion is introduced into the indexing equation multiplied by the unique impact factor being weighted via pairwise comparative method in Analytical Hierarchy processing. The majority of the workflow steps are automated via Graphical Modeler in QGIS utilizing open source spatial data, giving floor to further applicability of the method to similar cases. As a result, there are produced statistical and graphical information being useful for identifying wildfire prone forest surfaces within the metropolitan areas. Being applied into two different study areas, the results enable a comparative discussion and evaluation at regional scale. By utilizing open source software and data, this work contributes in the development of practical and re-applicable models of wildfire risk assessment promoting open access scientific culture. Finally, the study results successful in testing a rapid and cost free method for identifying the forest areas prone to wildfire ignition and spreading risk in metropolitan areas in support to disaster risk reduction agendas and sustainable Development Goals. Full article

The UNESCO Biosphere Reserves (BRs) comprise core conservation areas supported by a buffer and transition zone of sustainable development. This zoning can help manage urbanisation around conservation areas. Although it is UNESCO policy to measure the number of BRs that have interactions with urban areas, there has been no systematic assessment of urban biospheres since 2008. This research addresses this deficit by measuring the extent of urbanisation of all designated BRs within the European Union (EU). Using the Copernicus Urban Atlas, the proximity of BRs to Functional Urban Areas (FUA) was determined. The results show that 46% (76/167) of BRs are situated within FUAs, including 11% (18/167) entirely within an FUA. The majority (64%) of EU-28 countries have BRs within FUAs. Urban influences on EU-28 BRs are extensive, as 90% are found within 50 km of an FUA. However, integration with urban areas may be lacking as 14% of EU BRs were adjacent to an FUA. Urban pressures are acute for 11% of EU BRs which had multiple FUAs within a 50 km radius. Therefore, urbanisation of BRs is a widespread challenge and recommendations are provided for BRs to function as an information sharing network and develop a new urban strategy. Full article

This study examines the potential of open geodata sets and multitemporal Landsat satellite data as the basis for the automated generation of land use and land cover (LU/LC) information at large scales. In total, six openly available pan-European geodata sets, i.e., CORINE, Natura 2000, Riparian Zones, Urban Atlas, OpenStreetMap, and LUCAS in combination with about 1500 Landsat-7/8 scenes were used to generate land use and land cover information for three large-scale focus regions in Europe using the TimeTools processing framework. This fully automated preprocessing chain integrates data acquisition, radiometric, atmospheric and topographic correction, spectral–temporal feature extraction, as well as supervised classification based on a random forest classifier. In addition to the evaluation of the six different geodata sets and their combinations for automated training data generation, aspects such as spatial sampling strategies, inter and intraclass homogeneity of training data, as well as the effects of additional features, such as topography and texture metrics are evaluated. In particular, the CORINE data set showed, with up to 70% overall accuracy, high potential as a source for deriving dominant LU/LC information with minimal manual effort. The intraclass homogeneity within the training data set was of central relevance for improving the quality of the results. The high potential of the proposed approach was corroborated through a comparison with two similar LU/LC data sets, i.e., GlobeLand30 and the Copernicus High Resolution Layers. While similar accuracy levels could be observed for the latter, for the former, accuracy was considerable lower by about 12–24%. Full article