Global Human Settlement Research Articles

Assessing the relationship between morphology and mapping accuracy of built-up areas derived from global human settlement data

ABSTRACT It is common knowledge that the level of landscape heterogeneity may affect the performance of remote sensing based land use/land cover classification. While this issue has been studied in depth for land cover data in general, the specific relationship between the mapping accuracy and morphological characteristics of built-up surfaces has not been analyzed in detail, an urgent need given the recent emergence of a variety of global, fine-resolution settlement datasets. Moreover, previous studies typically rely on aggregated, broad-scale landscape metrics to quantify the morphology of built-up areas, neglecting the fine-grained spatial variation and scale dependency of such metrics. Herein, we aim to fill this knowledge gap by assessing the associations between localized (focal) landscape metrics, derived from binary built-up surfaces and localized data accuracy estimates. We tested our approach for built-up surfaces from the Global Human Settlement Layer (GHSL) for Massachusetts (USA). Specifically, we examined the explanatory power of landscape metrics with respect to both commission and omission errors in the multi-temporal GHS-BUILT R2018A data product. We found that the Landscape Shape Index (LSI) calculated in focal windows exhibits, on average, the highest levels of correlation to focal accuracy measures. These relationships are scale-dependent, and become stronger with increasing level of spatial support. We found that thematic omission error, as measured by Recall, has the strongest relationship to measures of built-up surface morphology across different temporal epochs and spatial resolutions. The results of our regression analysis (R2 > 0.9), estimating accuracy based on landscape metrics, confirmed these findings. Lastly, we tested the generalizability of our findings by regionally stratifying our regression models and applying them to a different version of the GHSL (i.e. the GHS-BUILT-S2) and a different study area. We observed varying levels of model transferability, indicating that the relationship between accuracy and landscape metrics may be sensor-specific, and is heavily localized for most accuracy metrics, but quite generalizable for the Recall measure. This indicates that there is a strong and generalizable association between morphological properties of built-up land and the degree to which it is “undermapped.”

A Review of Human Settlement Research on Climate Change Response under Carbon-Oriented: Literature Characteristics, Progress and Trends

Climate issues have affected the sustainable development of global human settlements, and carbon, an essential factor affecting climate change, has become a hotspot of academic concern. This paper analyzes the research characteristics, stages and planning of carbon-oriented climate change response research in human settlements based on the literature related to research on carbon-oriented human settlements for climate change, hereinafter referred to as RCHSCC, included in the Web of Science core database since 1991, using CiteSpace and VOSviewer bibliometric software. Based on the analysis of the literature and discipline distribution, research hotspots and priorities, this paper classifies the RCHSCC into four stages: early exploration, relationship building, integrated development and deepening collaboration. Based on keyword clustering, annual overlap and keyword emergence analysis, this paper predicts that future research will have three major trends regarding climate risk management, carbon technology enhancement and urban safety and resilience research. The study aims to analyze the distribution characteristics and evolution of research on carbon-oriented human settlements for climate change from 1991 to 2022. The RCHSCC predicts three major trends in the future—climate risk management, carbon technology upgrading and urban security and resilience—and offers three recommendations for governments and planners in terms of climate change adaptation and low-carbon and efficient development in human settlements.

Coastalization patterns in the Mediterranean: a spatiotemporal analysis of coastal urban sprawl in tourism destination areas

Under the current climate change scenery, coastalization, i.e. a linear-type of urban sprawl along the coastal zone, has become a key planning and policy concern at a global scale. Such a coastal urban sprawl trend seems to be further intensifying by, among others, mass tourism development in coastal destinations. This is evident by the abundance of relevant examples, being detected, among others, in the Mediterranean region. Taking into consideration the extreme pressure of the coastalization trend on land/marine ecosystems and its impacts on sustainability and resilience objectives, the focus of this research is on the development of a spatiotemporal and data-driven methodological approach that is capable of quantifying the dynamics of coastal urban sprawl. The proposed approach is grounded on a set of spatial metrics and a combination of the multitemporal Global Human Settlement Layer and the Corine Land Cover data; and is tested in four Mediterranean highly-rated tourism destinations in Spain, France, Italy and Greece. The variation of spatial metrics along different time spots and distinct distance buffers from the coast, unveils diversifying coastalization typologies and urban sprawl patterns; and provides input for more informed and place-based policy decisions on sustainable coastal land management. The proposed approach is easily replicable to other coastal regions as well for capturing/quantifying aspects of urban sprawl and coastalization dynamics and supporting relevant evidence-based decision-making.

Identifying Exposure of Urban Area to Certain Seismic Hazard Using Machine Learning and GIS: A Case Study of Greater Cairo

The 1992 Cairo earthquake, with a moment magnitude of 5.8, is the most catastrophic earthquake to shock the Greater Cairo (GC) in recent decades. According to the very limited number of seismological stations at that time, the peak ground acceleration (PGA) caused by this event was not recorded. PGA calculation requires identification of nature of the earthquake source, the geologic setting of the path between the source and site under investigation and soil dynamic properties of the site. Soil dynamic properties are acquired by geotechnical investigations and/or geophysical survey. These two methods are costly and are not applicable in regional studies. This study presents an adaptive and reliable PGA prediction model using machine learning (ML) along with six standard geographic information system (GIS) interpolation methods (IDW, Kriging, Natural, Spline, TopoToR, and Trend) to predict the spatial distribution of PGA caused by this event over the GC. The model is employed to estimate the exposure of the urban area and population in the GC based on the available geotechnical and geophysical investigations. The exposure (population) data is from free and easy-access sources, e.g., Landsat images and the Global Human Settlement Population Grid (GHS-POP). The results show that Natural, Spline, and Trend are not suitable GIS interpolation techniques for generating seismic hazard maps (SHMs), while the Kriging Method shows sufficient prediction. Interestingly, with an accuracy of 96%, the ML model outperforms the classical GIS methodologies.

Unsupervised domain adaptation for global urban extraction using Sentinel-1 SAR and Sentinel-2 MSI data

Accurate and up-to-date maps of built-up areas are crucial to support sustainable urban development. Earth Observation (EO) is a valuable data source to cover this demand. In particular, Sentinel-1 Synthetic Aperture Radar (SAR) and Sentinel-2 MultiSpectral Instrument (MSI) missions offer new opportunities to map built-up areas on a global scale. Using Sentinel-2 images, recent urban mapping efforts achieved promising results by training Convolutional Neural Networks (CNNs) on available built-up data. However, these results strongly depend on the availability of local reference data for fully supervised training or assume that the application of CNNs to unseen areas (i.e. across-region generalization) produces satisfactory results. To alleviate these shortcomings, it is desirable to leverage Semi-Supervised Learning (SSL) algorithms that can take advantage of unlabeled data, especially because satellite data is plentiful. In this paper, we propose a novel Domain Adaptation (DA) approach using SSL that jointly exploits Sentinel-1 SAR and Sentinel-2 MSI to improve across-region generalization for built-up area mapping. Specifically, two identical sub-networks are incorporated into the proposed model to perform built-up area segmentation from SAR and optical images separately. Assuming that consistent built-up area segmentation should be obtained across data modality, we design an unsupervised loss for unlabeled data that penalizes inconsistent segmentation from the two sub-networks. Therefore, we propose to use complementary data modalities as real-world perturbations for consistency regularization. For the final prediction, the model takes both data modalities into account. Experiments conducted on a test set comprised of sixty representative sites across the world showed that the proposed DA approach achieves strong improvements (F1 score 0.694) over fully supervised learning from Sentinel-1 SAR data (F1 score 0.574), Sentinel-2 MSI data (F1 score 0.580) and their input-level fusion (F1 score 0.651). To demonstrate the effectiveness of DA, we also performed a comparison with two state-of-the-art products, namely GHS-BUILT-S2 and WSF 2019, on the test set. The comparison showed that our model is capable of producing built-up area maps with comparable or even better quality than the state-of-the-art global human settlement maps. Therefore, the multi-modal DA offers great potential to be adapted to produce easily updateable human settlements maps at a global scale.

Measuring and analysing urban growth pattern using spatial metrics in Bengaluru, India

Assessment of urban growth is considered an important study for urban areas. The growth patterns and dynamics play a pivotal role in defining urban areas. Spatial metrics can prove to be instrumental in analysing these dynamics. The study focuses on categorizing urban growth and quantifying the growth compactness and speed of Bengaluru and Karnataka using built-up obtained from Global Human Settlement Layer (GHSL) and Nighttime Lights (NTLs) data, respectively. The evolution of Bengaluru and the skewed development in Karnataka are also observed. Landscape Expansion Index (LEI) is used to identify growth categories showing that 37.75% of Bengaluru’s growth was as edge expansion. Area-weighted mean Expansion Index (AWMEI), Urban Expansion Intensity Index (UEII) and Shannon’s entropy (SE) are used to quantify growth compactness, speed and entropy, respectively. The city has more growth compactness and speed compared to any other settlement in Karnataka. These urban growth dynamics shall help decision-makers in making data-driven decisions for urbanscape.

Urban emissions and land use efficiency scenarios towards effective climate mitigation in urban systems

There are critical mitigation opportunities in urban areas that can be better understood through urban level analyses in the context of climate scenarios. This research work develops an approach for emissions scenarios for specific urban areas based on Shared Socioeconomic and Representative Concentration Pathways with a focus on the green growth paradigm at the lowest radiative forcing outcomes. The scenario characteristic for compact urban form is further complemented with improvements in existing land use efficiencies that are based on the Global Human Settlement Layer. Urban emissions scenarios for 420 urban areas among those with the highest emissions footprint totaling about 10.7 GtCO2eq in 2020 and land use efficiency scenarios for the top 10 urban areas in 9 regions are analysed. The 90 urban areas that represent about 6.2 GtCO2eq of urban emissions and 83.3 thousand km2 of built-up area in 2020 are then compared in a two-dimensional scenario space. Co-benefits are quantified for a new 100% renewable energy scenario that represents electrification combined with sector coupling. Based on Monte Carlo simulations, random performances that near the most stringent emissions pathways in a range of ±10% can still approach net-zero emissions by 2050 in alignment with the temperature goal of the Paris Agreement. Yet there is a penalty of about 0.95 GtCO2 even in the best land use efficiency scenario due to possible impacts of urban land expansion on the sequestration potential of local biomes. The results have widespread ramifications for guiding urban areas towards integrated action for reducing emissions, limiting the growth in urban extent, and providing co-benefits for urban inhabitants when effective action is needed urgently.

A scale-sensitive framework for the spatially explicit accuracy assessment of binary built-up surface layers

To better understand the dynamics of human settlements, thorough knowledge of the uncertainty in geospatial built-up surface datasets is critical. While frameworks for localized accuracy assessments of categorical gridded data have been proposed to account for the spatial non-stationarity of classification accuracy, such approaches have not been applied to (binary) built-up land data. Such data differs from other data such as land cover data, due to considerable variations of built-up surface density across the rural-urban continuum resulting in switches of class imbalance, causing sparsely populated confusion matrices based on small underlying sample sizes. In this paper, we aim to fill this gap by testing common agreement measures for their suitability and plausibility to measure the localized accuracy of built-up surface data. We examine the sensitivity of localized accuracy to the assessment support, as well as to the unit of analysis, and analyze the relationships between local accuracy and density / structure-related properties of built-up areas, across rural-urban trajectories and over time. Our experiments are based on the multi-temporal Global Human Settlement Layer (GHSL) and a reference database for the state of Massachusetts (USA). We find strong variation of suitability among commonly used agreement measures, and varying levels of sensitivity to the assessment support. We then apply our framework to assess localized GHSL data accuracy over time from 1975 to 2014. Besides increasing accuracy along the rural-urban gradient, we find that accuracy generally increases over time, mainly driven by peri-urban densification processes in our study area. Moreover, we find that localized densification measures derived from the GHSL tend to overestimate peri-urban densification processes that occurred between 1975 and 2014, due to higher levels of omission errors in the GHSL epoch 1975.

Urban Sprawl in Poland (2016–2021): Drivers, Wildcards, and Spatial Externalities

The COVID-19 lockdown in 2020–2021 and the refugee crisis in 2021–2022 were two new and unexpected social and political events in Poland in recent years. These “wildcards” will certainly have major effects on individuals and cities, both directly and indirectly, through the influence of “externalities.” The paper examines trends in the spatial development of Polish cities during the last five years (2016–2021), focusing on residential suburbanization and urban sprawl. The study aims to reveal the elements that determine the spatial scale of suburbanization, as well as “wildcards” that may have an indirect impact on the process but are difficult to quantify and include in spatial analysis. The use of location quotient (LQ) metrics, as well as a subset of the Global Human Settlement Layer in the spatial analysis allow for comparisons of locations with intensified urbanization throughout different periods, serving a task that is comparable to feature standardization from a time and space viewpoint. The analysis provides evidence of growing suburbanization surrounding major Polish cities from 2016 to 2021, while also exposing distinct elements of spatial development during a period that was marked by social and political stress (2021).

Mass appraisal as affordable public policy: Open data and machine learning for mapping urban land values

Updated cadastral land values are a matter of critical importance for local governments: higher revenue of property taxes, more equitable treatment to taxpayers, a fundamental input in the design of public policies related to access to land and housing for the most vulnerable and a key feature in land value capture strategies to finance public infrastructure, to name just a few public policies that require correct valuations of land. However, in Latin America, outdated cadastral values are common to most cities. The reasons for this can be found in the complexity of the mass appraisal process, lack of institutional and fiscal capacity to undertake it and bureaucratic resistance to its implementation.The objective of this paper is to present a mass appraisal methodology that uses only free and open data to achieve robust urban land valuations. Information from the OpenStreetMap Project is used to generate several land variables. In addition, the Global Human Settlement Layer of the European Commission is used to determine the level of consolidation of urban sprawl. Land value data were obtained from the Mapa de Valores de América Latina, a collaborative initiative that systemizes more than 68,000 data from more than 900 cities.This information is used to train three tree-based machine learning models: Random Forest, Quantile Random Forest and Gradient Boosting Model. The results support the viability of the proposed strategy, simplifying the mass appraisal process in terms of costs, time and complexity of the information used.

Assessing the coastal vs hinterland divide by use of multitemporal data: Case study in Corinthia, Greece

Escalating urban development in coastal zones is currently noticed in many regions around the globe, leading to unsustainable future pathways. This, among others, accounts for: land and marine ecosystems’ degradation; higher vulnerability to Climate Change impacts; and a largely uneven urban network pattern, raising issues of the ‘coastal vs mainland’ divide that hampers a balanced regional development. This holds true in the case study explored in this work, namely Regional Unit of Corinthia, Region of Peloponnese, Greece. This area, despite its natural and cultural assets and critical location as a transportation node in close proximity to Athens, displays certain spatial imbalances as to the coastal and hinterland urban expansion pattern that are linked also to developmental ones. Monitoring urban development by use of multi-temporal data for built-up areas can advocate the identification/assessment of important spatial dimensions of the aforementioned problems; and guide evidence-based policy decisions. Along these lines, this paper elaborates on the development of a methodology in response to the research question of quantifying urban development trends and assessing the “coastal vs hinterland” divide. This methodology is grounded in high-resolution multi-temporal data processing for built-up areas, provided by the Global Human Settlement Layer (GHSL); and the estimation of a Coastal vs Hinterland divide Index (CHI). Results highlight the criticality of coastal urban development and the need for an integrated policy, re-directing developmental impulses towards the less privileged hinterland urban constellations; while supporting endogenous development of territorial assets, in order for a more balanced and sustainable urban settlements’ model to emerge.

Google Earth Engine for Large-Scale Flood Mapping Using SAR Data and Impact Assessment on Agriculture and Population of Ganga-Brahmaputra Basin

The Ganga-Brahmaputra basin is highly sensitive to the impacts of climate change and experiences recurrent flooding, which affects large agricultural areas and poses a high risk to the population. The present study is focused on the recent flood disaster in the Ganga-Brahmaputra basin, which mainly affected the regions of Bihar, West Bengal, and Assam in India and neighboring Bangladesh during July, August, and September 2020. Using the Sentinel-1A Synthetic Aperture Radar (SAR) data, the flood extent was derived in the Google Earth Engine (GEE) platform. The composite area under flood inundation for July–September was estimated to be 25,889.1 km2 for Bangladesh, followed by Bihar (20,837 km2), West Bengal (17,307.1 km2), and Assam (13,460.1 km2). The Copernicus Global Land Cover dataset was used to extract the affected agricultural area and flood-affected settlement. Floods have caused adverse impacts on agricultural lands and settlements, affecting 23.68–28.47% and 5.66–9.15% of these areas, respectively. The Gridded Population of the World (GPW) population density and Global Human Settlement Layer (GHSL) population dataset were also employed to evaluate flood impacts, which revealed that 23.29 million of the population was affected by floods in the Ganga-Brahmaputra basin. The highest impacts of floods can be seen from the Bihar state, as people reside in the lower valley and near to the riverbank due to their dependency on river water. Similarly, the highest impact was from Bangladesh because of the high population density as well as the settlement density. The study provided a holistic spatial assessment of flood inundation in the region due to the combined impact of the Ganga-Brahmaputra River basin. The identification of highly flood-prone areas with an estimated impact on cropland and build-up will provide necessary information to decision-makers for flood risk reduction, mitigation activities, and management.

Land use efficiency of functional urban areas: Global pattern and evolution of development trajectories

The application of last-generation spatial data modelling, integrating Earth Observation, population, economic and other spatially explicit data, enables insights into the sustainability of the global urbanisation processes with unprecedented detail, consistency, and international comparability. In this study, the land use efficiency indicator, as developed in the Sustainable Development Goals, is assessed globally for the first time at the level of Functional Urban Areas (FUAs). Each FUA includes the city and its commuting zone as inferred from statistical modelling of available spatial data. FUAs represent the economic area of influence of each urban centre. Hence, the analysis of land consumption within their boundary has significance in the fields of spatial planning and policy analyses as well as many other research areas. We utilize the boundaries of more than 9,000 FUAs to estimate the land use efficiency between 1990 and 2015, by using population and built-up area data extracted from the Global Human Settlement Layer. This analysis shows how, in the observed period, FUAs in low-income countries of the Global South evolved with rates of population growth surpassing the ones of land consumption. However, in almost all regions of the globe, more than half of the FUAs improved their land use efficiency in recent years (2000–2015) with respect to the previous decade (1990–2000). Our study concludes that the spatial expansion of urban areas within FUA boundaries is reducing compactness of settlements, and that settlements located within FUAs do not display higher land use efficiency than those outside FUAs.

Mapping High-Resolution Global Impervious Surface Area: Status and Trends

Impervious surface area (ISA) mapping at the global scale has entered a new era. Currently, the number of high-resolution global ISA products is gradually increasing; however, a literature review that systematically investigates these ISA products is still lacking, which limits the application of these products. Thus, we provide a comprehensive analysis of the existing high-resolution global ISA products, concentrating on the aspects of the data sources, training samples, features, and methods. Moreover, we evaluate these products at multi-temporal and multi-spatial scales, using a series of independent test samples. The results demonstrate that the multi-temporal accuracy of the ISA products presents an increasing trend, due to the increase of the available sensors. Among the continuous time-series products (e.g., the updated new global impervious surface area (GISA 2.0), the global impervious surface area (GISA), global annual urban dynamics (GAUD), Global Human Settlement Layer (GHSL), and global artificial impervious areas (GAIA)), the accuracy of the GISA 2.0 outperforms the others at global, continental, and regional scales. However, the mapping performance of these products in small towns and arid and rural regions needs to be enhanced. In particular, we focus on the spatio-temporal disagreement of the ISA products. We show that the high disagreement regions are predominantly concentrated in eastern Asia, western Europe, and eastern North America. In addition, the high disagreement regions are characterized by low ISA density, high vegetation coverage, and high albedo bare ground coverage. Additionally, this paper concludes with some remarks about the future directions of global ISA mapping.

Foreign experience in using Earth remote sensing data in studying urban development

Earth remote sensing data are actively used in studying the development of cities abroad and much less in Russia. The aim of the study was to generalize the foreign experience of using Earth remote sensing data in the study of urban development in social geography and related studies. The experience of predecessors is analyzed. Practically absence of the use of Earth remote sensing data by domestic geographers and social scientists in the study of cities has been revealed. Two types of use of Earth remote sensing data in studying cities abroad are singled out: independent processing of satellite images of specific territories and the use of ready-made sets of processed Earth remote sensing data from large research groups. Both options for using Earth remote sensing data make it possible to compare the territories of the Earth, avoid administrative boundaries and the complexity of statistical accounting, and use longer time series. The differences lie in the depth of the information received and accessibility for the average user. Self-processed Earth remote sensing data are more informative, while ready-made sets are easier to use by ordinary users and with their help it is easier to compare different territories due to the universal methodology. Of the ready-made sets, the data of the Global Human Settlement Layer project from the European Commission stand out for the best quality, as well as the frequency of use.

Urban growth and land subsidence: Multi-decadal investigation using human settlement data and satellite InSAR in Morelia, Mexico

Limited attention is typically paid to the cause-effect relationship between land subsidence due to aquifers overexploitation in expanding metropolises and urban growth models and patterns. This paper implements an integrated urban and satellite Interferometric Synthetic Aperture Radar (InSAR) approach to investigate subsidence, multi-decadal urban growth and peopling trends in the Metropolitan Area of Morelia (ZMM) in the Mexican state of Michoacán. Stacking of JRC's Global Human Settlement Layer, DLR's World Settlement Footprint and INEGI's National Geostatistical Framework datasets reveals a predominant edge-expansion growth model, with urban densification in 1975–2020 and some sprawling in 1990–2000. Population of the ZMM doubled in the last 30 years, reaching over 1 million inhabitants. The ENVISAT and Sentinel-1 InSAR analysis confirms that subsidence is structurally-controlled by the main normal faults within the Cuitzeo half-graben. Differential sinking and ground discontinuities are aligned with buried tectonic faults and contrasting compressible sediment thickness. Non-linearly deforming subsidence bowls develop at extraction wells in both old and newly urbanized sectors of the ZMM. Maximum vertical displacement velocities increased from −2.5 cm/year in 2003−2010 to −9.0 cm/year in 2014–2021, with subsidence migrating towards recently urbanized zones. More than 250 new groundwater wells were added to the public registry since 2000, many of which within new urban sectors. Time-lapse InSAR reveals a 4 km2 rapidly subsiding bowl that formed at the largest social housing neighbourhood of Villas del Pedregal, as building lots were progressively completed and sold, and new wells registered. With angular distortions due to the differential subsidence reaching 0.12% in 2014–2021, new buildings and roads are exposed to fracturing and surface faulting risk of comparable level as the city historic building blocks located along the main faults. By providing useful insights into the relationship between urban growth and land subsidence in the ZMM, the approach proves valuable for application to other metropolises worldwide.

Demystifying the Geography of Urbanization in Indonesia (Case Study: Southeast Sulawesi Province)

What is a city? How can we define the boundaries of a city? Despite being the popular subject of research in rapid urbanization, the discussion of the spatial dimension of urbanization is still few in Indonesia. This paper aims to make the geography of urbanization in Indonesia clearer and easier to understand by applying the degree of urbanization (DEGURBA/DoU) methodology. The case study location proposed is Southeast Sulawesi province (SSP)which has distinctive urbanization trajectories with rapid urbanization trends and a relatively stable rural population, but a lack of research regarding its urbanization patterns. This research used Global Human Settlement Layer (GHSL) data as the primary source to conduct spatial analysis to produce the degree of urbanization with complementary national statistical and spatial data to compare with the current conventional classification methodology. The findings show that based on DoU, 39.87% of the SSP population live in urban centers, 22.58% in urban cluster/periurban, and 37.56%. SSP has a total of 49 urban units with 6 urban centers/cities, and 43 urban clusters/towns. By testing the rank-size distribution of SSP’s urban units, it shows that SSP’s urban systems pattern satisfy Zipf’s law with R2 = .9885, and the slope of the fitted line is -0.9554. Based on this result, currently, SSP has 4 medium cities, 2 small cities, 21 large towns, and 20 small towns. In addition, the DoU also has an opportunity to understand how should urban areas be governed and managed, and by whom. Despite the Greater Kendari Urban Area has not yet fulfilled the national’s Metropolitan population threshold (1 million pop), the urban management and development should be conducted more collaboratively with neighboring regencies such as Konawe and South Konawe. The advantages are shown by these findings highlight the importance of scaling up this research to a national scale and using finer and nationally available data.

Air Quality Estimation in Ukraine Using SDG 11.6.2 Indicator Assessment

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., PM2.5 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.

The UrbEm Hybrid Method to Derive High-Resolution Emissions for City-Scale Air Quality Modeling

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.

Residential building stock modelling for mainland China targeted for seismic risk assessment

Abstract. To enhance the estimation accuracy of economic loss and casualty in seismic risk assessment, a high-resolution building exposure model is necessary. Previous studies in developing global and regional building exposure models usually use coarse administrative-level (e.g. country or sub-country level) census data as model inputs, which cannot fully reflect the spatial heterogeneity of buildings in large countries like China. To develop a high-resolution residential building stock model for mainland China, this paper uses finer urbanity-level population and building-related statistics extracted from the records in the tabulation of the 2010 population census of the People's Republic of China (hereafter abbreviated as the “2010 census”). In the 2010 census records, for each province, the building-related statistics are categorized into three urbanity levels (urban, township, and rural). To disaggregate these statistics into high-resolution grid level, we need to determine the urbanity attributes of grids within each province. For this purpose, the geo-coded population density profile (with 1 km × 1 km resolution) developed in the 2015 Global Human Settlement Layer (GSHL) project is selected. Then for each province, the grids are assigned with urban, township, or rural attributes according to the population density in the 2015 GHSL profile. Next, the urbanity-level building-related statistics can be disaggregated into grids, and the 2015 GHSL population in each grid is used as the disaggregation weight. Based on the four structure types (steel and reinforced concrete, mixed, brick and wood, other) and five storey classes (1, 2–3, 4–6, 7–9, ≥10) of residential buildings classified in the 2010 census records, we reclassify the residential buildings into 17 building subtypes attached with both structure type and storey class and estimate their unit construction prices. Finally, we develop a geo-coded 1 km × 1 km resolution residential building exposure model for 31 provinces of mainland China. In each 1 km × 1 km grid, the floor areas of the 17 residential building subtypes and their replacement values are estimated. The model performance is evaluated to be satisfactory, and its practicability in seismic risk assessment is also confirmed. Limitations of the proposed model and directions for future improvement are discussed. The whole modelling process presented in this paper is fully reproducible, and all the modelled results are publicly accessible.