European Settlement Map Research Articles

The Impact of Portuguese Community Pharmacies on Influenza Vaccination Accessibility and Coverage

Abstract Background Vaccination reduces influenza infections. Non-adherence often stems from external barriers like distance to vaccination sites. To counteract the projected coverage decrease for 2023/24, Portugal’s Government expanded free Influenza vaccination to those ≥ 60 and to community pharmacies (CP), enhancing proximity and availability. This study aimed to assess the impact of CP inclusion on the distance to vaccination sites and on Influenza vaccine coverage in the 2023/24 campaign. Methods Analytical study, evaluating the average distance (km) from population to the nearest vaccination site per municipality using GPS coordinates from National Health Service (NHS) sites and CP. The Global Human Settlement resident population grid was derived from Eurostat 2011 Census, Corine Land Cover Refined 2006 and European Settlement Map 2016. Scenarios with and without CP were compared. The difference (%) in distances between 2022/23 (NHS sites) and 2023/24 (NHS sites and CP) and the coverage for the top and bottom 10% of municipalities with the highest and lowest distance reductions were calculated. Results In the 2023/24 Influenza vaccination campaign, 844 NHS sites and 2487 CP participated. Excluding CP, the average distance to the nearest NHS site was 2.4 km, decreasing to 1.2 km with CP inclusion (-50.5%). As of March 31, 2024, influenza vaccine coverage for those aged ≥65 was 72.1%, decreasing 0.1 percentage points (pp) compared to 2022/23 (72.2%). For the top 10% municipalities with the highest distance reduction (up to 66.5%), coverage increased by 1.3 pp compared to 2022/23. Conversely, the bottom 10% (reduction under 10.3%) experienced a decrease of 4.5 pp. Conclusions The inclusion of pharmacies has effectively reduced access barriers, filled geographical gaps, and improved proximity to vaccination. This strategy appears to have a positive impact on influenza vaccination coverage, particularly amid growing hesitancy, with potential for broader public health strategies. Key messages • The inclusion of community pharmacies in the 2023/24 Influenza vaccination campaign reduced the average distance to the nearest vaccination site by 50.5%, from 2.4 km to 1.2 km. • In 2023/24, for the top 10% municipalities with the highest distance reduction (up to 66.5%), coverage increased 1.3 pp compared to 2022/23.

Application of Satellite Data for Estimating Rooftop Solar Photovoltaic Potential

Rooftop solar photovoltaics can significantly contribute to global energy transitions by providing clean, decentralized energy without the need for new land, thereby avoiding land-use conflicts. It serves as a valuable complement to other renewable-energy sources and is expected to play a crucial role in future electricity systems. Due to the spatiotemporal variability in the solar radiation on roof surfaces, it is essential to determine the potentials of the rooftop photovoltaics and its variations in specific regions. In Germany, this potential was assessed in 5 km × 5 km zones, as well as at the federal-state and national levels. High-quality satellite and reanalysis data were used to determine the power output of the solar photovoltaics. Additionally, high-resolution (2 m × 2 m) European Settlement Map data, calibrated with solar cadastre data, were utilized to evaluate different scenarios. The results show that the potential is concentrated in larger urban areas and the southwestern part of Germany due to the availability of rooftop space and solar radiation. Overall, the national rooftop areas are substantial across all scenarios, ranging from 2100 to 4500 km2. The applied methods and scenarios provide a straightforward way to reveal the spatiotemporal variability and define realistic ranges of the solar photovoltaic potential without requiring detailed information about each building. However, assessing the rooftop photovoltaic potential remains challenging and uncertain due to the lack of large-scale, high-resolution data on building characteristics and the complexity of the solar radiation distribution in urban environments.

TOWARDS A PAN-EU BUILDING FOOTPRINT MAP BASED ON THE HIERARCHICAL CONFLATION OF OPEN DATASETS: THE DIGITAL BUILDING STOCK MODEL - DBSM

Abstract. This paper presents a hierarchical conflation process applied to open datasets for the creation of a seamless pan-European map of building footprints in vector format, named Digital Building Stock Model – DBSM. The objective is the sequential addition of input components (which currently include OpenStreetMap, Microsoft GlobalML Building Footprints, European Settlement Map), taking into account their limitations, and aiming at the highest level of completeness possible, for planning and evaluating energy transition scenarios at the EU level. The results indicate how DBSM compares robustly against cadastral data from Estonia, used as reference area. The comparison of DBSM with GHS-BUILT-S, a 10 metres resolution grid with worldwide coverage that encodes the built-up surface in each pixel as derived from Sentinel-2 imagery for the year 2018, reveals a relative overestimation of the latter, factored by 0.68 at the EU scale for a sound match.

Geospatial Intelligence and Machine Learning Technique for Urban Mapping in Coastal Regions of South Aegean Volcanic Arc Islands

Coastal environments are globally recognized for their spectacular morphological characteristics as well as economic opportunities, such as fisheries and tourism industries. However, climate change, growth in tourism, and constant coastal urban sprawl in some places result in ever-increasing risk in the islands of the South Aegean Volcanic Arc (SAVA), necessitating thoughtful planning and decision making. GEOspatial INTelligence (GEOINT) can play a crucial role in the depiction and analysis of the natural and human surroundings, offering valuable information regarding the identification of vulnerable areas and the forecasting of urbanization rates. This work focuses on the delineation of the coastal zone boundaries, semi-automatization of Satellite-Derived Bathymetry (SDB), and urban mapping using a machine learning algorithm. The developed methodology has been implemented on the islands of Thira (Santorini island complex) and Milos. This study attempts to identify inaccuracies in existing open-source datasets, such as the European Settlement Map (ESM), as a result of the unique combination of the architectural style and bare-soil characteristics of the study areas. During the period 2016–2021, the average accuracy of the developed methodology for urban mapping in terms of the kappa index was 80.15% on Thira and 88.35% on Milos. The results showed that the average urbanization expansion on specified settlements was greater than 22% for both case studies. Ultimately, the findings of this study could contribute to the effective and holistic management of similar coastal regions in the context of climate change adaptation, mitigation strategies, and multi-hazard assessment.

Assessing the suitability of urban-oriented land cover products for mapping rural settlements

In recent years, new settlement mapping products have become available at the global and continental scale. Although accuracy assessments have indicated the high quality of these products, assessments were performed mainly on urban areas. However, there is also a need to monitor rural settlement development, as it is often located in proximity to biodiversity hotspots. In this paper, we verified the suitability of three settlement products (i.e., Global Urban Footprint – GUF, European Settlement Map – ESM and Open Street Map – OSM) to detect rural settlements in the Carpathian ecoregion. Two independent accuracy assessments indicated that the GUF captures rural settlements most effectively (overall accuracy – OA – 65.4% and 92.5% depending on the procedure). In contrast, the ESM overestimated settlements (OA – 49.5% and 90.8%), while the OSM (OA – 61.2% and 90.2%) was the most inconsistent source of settlement data. A regional comparison indicated some deviations from these accuracies, reflecting the variability of settlement structures within the study area. This study highlights that although the GUF was the best-performing product in mapping rural settlements across the whole study area, the settlement information it provided was rather conservative, and rural settlements are still insufficiently represented in all tested datasets.

Climate Services to Support Disaster Risk Reduction and Climate Change Adaptation in Urban Areas: The CLARITY Project and the Napoli Case Study

Climate services are emerging worldwide as an essential tool to bridge the advancement in climate science and meteo/earth observations with a variety of operational fields in the domains of Disaster Risk Reduction (DRR) and Climate Change Adaptation (CCA). It is multidisciplinary study area with promising applications in the field of urban microclimate simulations, supporting climate-resilient redevelopment actions at both the city and neighborhood levels. The CLARITY CSIS (Climate Services Information System, available at https://csis.myclimateservice.eu/), developed within the H2020 CLARITY project, is an innovative hazard/impact modeling tool that takes into account short- to long-term climate change scenarios and urban microclimate variability. Disaster risks associated with climate change, such as heat waves and floods, are concentrated in limited periods of the year and therefore not adequately represented by annually averaged values. To this aim, new datasets have been extracted from Regional Climate Models to estimate the frequency of extreme temperatures and precipitation events until 2100, and a novel modeling methodology has been developed to capture the effect on the urban microclimate due to specific built environment features. The wide amount of data generated by satellite earth observations and made available at pan-European level through the Copernicus datasets (e.g., Urban Atlas, European Settlement Map, etc.) has been processed through specific algorithms and GIS spatial analysis tools to extract detailed information related to key parameters linked to urban morphology and surface types. In addition to the “screening service” available at the pan-European level through the CLARITY CSIS, an “expert service” workflow allows increasing the resolution of hazard and impact simulations at 250 m, by exploiting detailed land use datasets provided by local end-users and assessing the DRR/CCA potential of city-wide adaptation plans, as well as of specific district redevelopment projects. This paper will present the features of CLARITY CSIS and the results of Expert Services implemented for the City of Naples, focusing on the methods adopted to implement hazard/impact assessments and how information from climate services is tailored to support the integration of different DRR/CCA strategies within urban plans and projects.

Application of the Symbolic Machine Learning to Copernicus VHR Imagery: The European Settlement Map

This letter introduces the new European Settlement Map (ESM) production workflow, presents some indicatory results, and discusses the validation process. Unlike the previous ESM versions, the built-up (BU) extraction is realized through supervised learning (not only by means of image filtering and processing techniques) based on textural and morphological features. Input data are the Copernicus very-high-resolution image collection for the reference year 2015 coming from a variety of sensors. The workflow is fully automated, and it does not include any postprocessing. For the first time, a new layer containing nonresidential buildings was derived by using only remote sensing imagery and training data. The produced BU map is delivered at 2-m-pixel resolution (level-1 layer), while the residential/nonresidential layer (level 2) is delivered at 10-m spatial resolution. More than 46 000 scenes were processed, and around 6 million km2 of the European territory was mapped. The workflow was executed on the JRC Big Data platform. The validation showed a balanced accuracy of 0.81 and 0.91 for level 1 and 2 layers, respectively, and 0.71 for only the nonresidential layer.

APPROACHES TO DISTINGUISH ‘REAL’ CHANGES FROM ‘UNREAL’ CHANGES BASED ON MULTI-TEMPORAL 2D BUILDING FOOTPRINT DATA

Abstract. Building footprint data from National Mapping and Cadastral Agencies are available in Germany for 7 years as a uniform, nation-wide geospatial data set and are updated annually. These multi-temporal building data sets can form the basis for the application of change detection techniques to derive national figures on dynamics in the building stock. Since these building data sets have only been built up in recent years, it is necessary to distinguish real changes from false changes. This is done by applying vector geometry-based operations and statistical analyses, which are presented in this article. Furthermore, by the additional use of the raster dataset Copernicus – European Settlement Map (classified, resolution 2.5 m) it is approximately possible to estimate whether it is a correct change or not. The advantage of this approach is that large-scale comparable results can be derived simply and quickly based on uniform basic data.

A New European Settlement Map From Optical Remotely Sensed Data

An application of a general methodology for processing very high-resolution imagery to produce a European Settlement Map (ESM) in support of policy-makers is presented. The process mapped around 10 million ${\text{km}}^{2}$ of the European continent. The input image data are satellite SPOT-5/6 pan-sharpened multispectral images of 2.5- and 1.5-m spatial resolution, respectively. This is the first time that remote sensing technology demonstrates capability to produce a continental information layer using 2.5-m input images. Moreover, it is the highest resolution continental map produced so far. The presented workflow is data-driven and consists in fully automatic image information extraction based on textural and morphological image analysis. The learning method allows the processing of high-resolution image data using coarse resolution thematic layers as reference. Validation shows an overall accuracy of 96% with omission and commission errors less than 4% and 1%, respectively.