Urban Mapping Research Articles

Urban flood susceptibility mapping based on social media data in Chengdu city, China

Increase in urban flood hazards has become a major threat to cities, causing considerable losses of life and in the economy. To improve pre-disaster strategies and to mitigate potential losses, it is important to make urban flood susceptibility assessments and to carry out spatiotemporal analyses. In this study, we used standard deviation ellipse (SDE) to analyze the spatial pattern of urban floods and find the area of interest (AOI) based upon related social media data that were collected in Chengdu city, China. We used the social media data as the response variable and selected 10 urban flood-influencing factors as independent variables. We estimated the susceptibility model using the Naïve Bayes (NB) method. The results show that the urban flood events are concentrated in the northeast-central part of Chengdu city, especially around the city center. Results of the susceptibility model were checked by the Receiver Operating Characteristic (ROC) curve, showing that the area under the curve (AUC) was equal to 0.8299. This validation result confirmed that the susceptibility model can predict urban flood with a satisfactory accuracy. The urban flood susceptibility map in the city center area provides a realistic reference for flood monitoring and early warning.

Utilization of Hyperspectral and Multispectral Images for Producing Urban Maps by Remote Sensing and GIS Techniques

Utilization of Hyperspectral and Multispectral Images for Producing Urban Maps by Remote Sensing and GIS Techniques

Evaluation and Comparison of Open and High-Resolution LULC Datasets for Urban Blue Space Mapping

Blue spaces (or water bodies) have a positive impact on the built-up environment and human health. Various open and high-resolution land-use/land-cover (LULC) datasets may be used for mapping blue space, but they have rarely been quantitatively evaluated and compared. Moreover, few studies have investigated whether existing 10-m-resolution LULC datasets can identify water bodies with widths as narrow as 10 m. To fill these gaps, this study evaluates and compares four LULC datasets (ESRI, ESA, FROM-GLC10, OSM) for blue space mapping in Great Britain. First, a buffer approach is proposed for the extraction of water bodies of different widths from a reference dataset. This approach is applied to each LULC dataset, and the results are compared in terms of accuracy, precision, recall, and the F1-score. We find that a high median accuracy (i.e., >98%) is achieved with all four LULC datasets. The OSM dataset gives the best recall and F1-score. Both the ESRI and ESA datasets produce better results than the FORM-GLC10 dataset. Additionally, the OSM dataset enables the identification of water bodies with widths of 10 m, whereas only water bodies with widths of 20 m or more can be identified in the other datasets. These findings may be beneficial for urban planners and designers in selecting an appropriate LULC dataset for blue space mapping.

Impact of urban parameterization and integration of WUDAPT on the severe convection

Amplified rates of urban convective systems pose a severe peril to the life and property of the inhabitants over urban regions, requiring a reliable urban weather forecasting system. However, the city scale's accurate rainfall forecast has constantly been a challenge, as they are significantly affected by land use/ land cover changes (LULCC). Therefore, an attempt has been made to improve the forecast of the severe convective event by employing the comprehensive urban LULC map using Local Climate Zone (LCZ) classification from the World Urban Database and Access Portal Tools (WUDAPT) over the tropical city of Bhubaneswar in the eastern coast of India. These LCZs denote specific land cover classes based on urban morphology characteristics. It can be used in the Advanced Research version of the Weather Research and Forecasting (ARW) model, which also encapsulates the Building Effect Parameterization (BEP) scheme. The BEP scheme considers the buildings' 3D structure and allows complex land–atmosphere interaction for an urban area. The temple city Bhubaneswar, the capital of eastern state Odisha, possesses significant rapid urbanization during the recent decade. The LCZs are generated at 500 m grids using supervised classification and are ingested into the ARW model. Two different LULC dataset, i.e., Moderate Resolution Imaging Spectroradiometer (MODIS) and WUDAPT derived LCZs and initial, and boundary conditions from NCEP GFS 6-h interval are used for two pre-monsoon severe convective events of the year 2016. The results from WUDAPT based LCZ have shown an improvement in spatial variability and reduction in overall BIAS over MODIS LULC experiments. The WUDAPT based LCZ map enhances high-resolution forecast from ARW by incorporating the details of building height, terrain roughness, and urban fraction.

The morphogenesis of villages-in-the-city: Mapping incremental urbanism in Hanoi city

Cities in the context of what is considered as the Global South are experiencing rapid urbanisation through which various villages have been incorporated into the city and became Villages-in-the-City (ViCs). While ViCs are here to stay, little is known about their morphogenesis as they have generally been considered as part of the negative image of the city and the related planning policies often tend to merely focus on eradication. Deploying urban mapping as a key method and using the Google Earth database, this study explores the morphogenesis of ViCs and maps the incremental transformations of building footprints and access networks across three case studies in Hanoi city, Vietnam. The findings of this paper can inform the ways in which the built environment professions can engage with the upgrading and integration of ViCs. The outcome of this study also challenges the conventional narratives and constructed images that simply consider ViCs as haphazard and disordered settlements.

A novel deep deterministic policy gradient model applied to intelligent transportation system security problems in 5G and 6G network scenarios

Traffic congestion has been an actual problem in large cities, causing personal inconvenience and environmental pollution. To solve this problem, new applications for Intelligent Transportation System (ITS) have been created, to monitor actual traffic conditions. Therefore, fast, reliable and safe systems are desirable for creating a real intelligent transportation environment. Deep learning algorithms have been proposed for a better understanding of traffic behavior from a security-related perspective. Thus, we aim to maximize the safety problems using a deep learning algorithm, where a novel policy gradient model is presented for detecting vehicular misuse. The proposed model uses a triple network replay algorithm, maximizing the network convergence speed. Three networks are selected to optimize the policy network variables. Finally, the replay algorithm is partitioned with the aim of obtaining a faster model. Simulations on a real urban map are performed in a scenario with the integration of 5G or 6G networks. An architectural model for the integration of a Vehicular Ad-hoc Network (VANET) and cellular networks is determined in software-defined networking (SDN). The results show that the accuracy prediction of the proposed system presents better performance compared to related studies, where the proposed model increases its convergence speed and cumulative reward. Thus, the ITS improvement by the proposed deep learning algorithm increases the prediction accuracy, and reduces the transmission delay, treating the traffic path according to the congestion.

A Positive-Unlabeled Learning Algorithm for Urban Flood Susceptibility Modeling

Flood susceptibility modeling helps understand the relationship between influencing factors and occurrence of urban flooding and further provides spatial distribution of flood risk, which is critical for flood-risk reduction. Machine learning methods have been widely applied in flood susceptibility modeling, but traditional supervised learning requires both positive (flood) and negative (non-flood) samples in model training. Historical flood inventory data usually contain positive-only data, whereas negative data selected from areas without flood records are prone to be contaminated by positive data, which is referred to as case-control sampling with contaminated controls. In order to address this problem, we propose to apply a novel positive-unlabeled learning algorithm, namely positive and background learning with constraints (PBLC), in flood susceptibility modeling. PBLC trains a binary classifier from case-control positive and unlabeled samples without requiring truly labeled negative data. With historical records of flood locations and environmental covariates, including elevation, slope, aspect, plan curvature, profile curvature, slope length factor, stream power index, topographic position index, topographic wetness index, distance to rivers, distance to roads, land use, normalized difference vegetation index, and precipitation, we compared the performances of the traditional artificial neural network (ANN) and the novel PBLC in flood susceptibility modeling in the city of Guangzhou, China. Experimental results show that PBLC can produce more calibrated probabilistic prediction, more accurate binary prediction, and more reliable susceptibility mapping of urban flooding than traditional ANN, indicating that PBLC is effective in addressing the problem of case-control sampling with contaminated controls and it can be successfully applied in urban flood susceptibility mapping.

A census from heaven: Unraveling the potential of deep learning and Earth Observation for intra-urban population mapping in data scarce environments

Urban population distribution maps are vital elements for monitoring the Sustainable Development Goals, appropriately allocating resources such as vaccination campaigns, and facilitating evidence-based decision making. Typically, population distribution maps are derived from census data from the region of interest. Nevertheless, in several low- and middle-income countries, census information may be unreliable, outdated or unsuitable for spatial analysis at the intra-urban level, which poses severe limitations in the development of urban population maps of adequate quality. To address these shortcomings, we deploy a novel framework utilizing multisource Earth Observation (EO) information such as Sentinel-2 and very-high-resolution Pleiades imagery, openly available building footprint datasets, and deep learning (DL) architectures, providing end-to-end solutions to the production of high quality intra-urban population distribution maps in data scarce contexts. Using several case studies in Sub-Saharan Africa, namely Dakar (Senegal), Nairobi (Kenya) and Dar es Salaam (Tanzania), our results emphasize that the combination of DL and EO data is very potent and can successfully capture relationships between the retrieved image features and population counts at fine spatial resolutions (100 meter). Moreover, for the first time, we used state-of-the-art domain adaptation methods to predict population distributions in Dar es Salaam and Nairobi (R 2 = 0.39, 0.60) that did not require national census or survey data from Kenya or Tanzania, but only a sample of training locations from Dakar. The DL architecture is based on a modified ResNet-18 model with dual-streams to analyze multi-modal data. Our findings have strong implications for the development of a new generation of urban population products that are an output of end-to-end solutions, can be updated frequently and rely completely on open data. • An EO-DL framework to model intra-urban population in the face of data scarcity. • Building footprints is a key variable to ensure transferability in areas with no local data. • A consistency-based domain adaptation enhances the transferability outcome. • Sentinel-2 imagery has good potential to retrieve fine-scale population patterns.

Interoperability of -Band Sentinel-1 SAR and GRACE Satellite Sensors on PSInSAR-Based Urban Surface Subsidence Mapping of Varanasi, India

The Sentinel-1 is an active synthetic aperture radar (SAR) satellite with a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${C}$ </tex-math></inline-formula> -band SAR sensor operating at a center frequency of 5.405 GHz and a wavelength of 5.55 cm. With the availability of freely available SAR datasets from Sentinel-1, the persistent scatterer SAR interferometry (PSInSAR)-based urban surface subsidence mapping has become easy. However, apart from geological causes, the major cause of urban surface subsidence is the over-exploitation of groundwater that results in piezometric pressure loss in the aquifers resulting in net subsidence. With the Gravity Recovery and Climate Experiment (GRACE) satellite sensor, the groundwater level fluctuations can be very easily studied temporally. But the coarse spatial resolution of GRACE data makes the study of groundwater fluctuations difficult to study for smaller watersheds. This study aims to correlate the PSInSAR average surface line of sight (LOS) displacement from Sentinel-1, with the average groundwater fluctuations from the GRACE sensor temporally from May 2017 to February 2022. The study also compared the correlation between PSInSAR displacement in both VV and VH polarizations and observed the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${R} ^{{2}}$ </tex-math></inline-formula> values to be 0.63 and 0.65 for VV and VH polarizations, respectively, with GRACE data. After that, using the displacement and groundwater level fluctuation data, an estimation of a gravimetric anomaly due to a decrease in groundwater level was carried out for Varanasi city. The <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${R} ^{{2}}$ </tex-math></inline-formula> , mean absolute error (MAE), and root-mean-square error (RMSE) were observed to be 0.84, 1.23, and 2.4, respectively, in gravimetric anomaly estimation, thus giving sufficient acceptance for interoperability of the two sensors.

Urban Wind Field Mapping Technique for Municipal Environmental Planning: A Case Study of Cheongju-Si, Korea

The integrated management of land and environmental plans (EPs) has been a key direction of sustainable policy in Korea. Although local EPs should be established based on spatial data, much of the pertinent environmental information has not been mapped. Accordingly, we develop a wind field mapping technique for establishing local EPs. This method was developed via a numerical weather prediction model (Weather Research and Forecast—WRF), and a diagnostic meteorological model (California Meteorological model—CALMET), based on weather observation data, as well as terrain and land cover data. The developed method was applied to a case study in Cheongju-si, Korea, for verifying its effectiveness. The created wind field maps of Cheongju-si provided an intuitive visual representation of the spatial distribution of seasonal and daytime/nighttime wind patterns. Such data helped understand the directionality and patterns of winds blowing into a city, or identify stagnant areas of wind circulation. Overlaying the wind field map with maps of air pollutant emissions revealed that pollutants from industrial areas could flow along the prevailing winds into residential areas downtown. These maps also implied that open spaces, such as parks and grasslands, were recommendable for stagnant areas, instead of creating industrial complexes or residential areas.

Meteorological zoning strategy for high-density cities based on urban climatic map

There is a clear link between the climate of a settlement and its potential sustainability. The development of cities should pay attention to the local geographic and topographic characteristics and the advantages (disadvantages) it brings, using as little artificial energy consumption as possible to improve the urban environment and the comfort of buildings. Environmental climate map is one of the important tools for urban meteorological zoning, which can play an effective technical support role for ecological planning and design of cities, especially high-density cities. This paper describes the meteorological characteristics of high-density cities and proposes the application of ambient climate map in heat island ventilation zoning and color zoning of high-density cities, aiming to provide research ideas for ecological zoning of high-density cities.

GEOSCENE MODELING AND ANALYSIS FOR URBAN FUNCTIONAL ZONES

Abstract. Urban functional zones are basic units for human socio-economic activities in cities. Urban functional zones play an important role in a wide range of urban studies, as they are fundamental to urban planning and management, urban resource allocation, as well as urban sustainable development evaluation. However, existing studies of urban functional zone modeling and mapping mainly employ state-of-the-art image segmentation and deep learning classification methods in computer science field to delineate and recognize urban functional zones, but ignore their heterogeneity, integration, multi-scale, and locality characteristics, so that the accuracy of urban functional zone modeling, mapping, and analysis is limited and interpretable, thus cannot comprehensively meet the requirements of downstream applications. This study, however, proposes a geoscene modeling and analysis framework for extracting urban functional zones, which first delineates urban functional zones by multiscale geoscene segmentation, then characterizes them by hierarchical semantic cognition, and finally recognizes their functional categories by linear Dirichlet mixture model. Although this paper does not present the detailed implementations of the geoscene modeling of urban functional zones, it illustrates the importance and the generally strategy of geoscene modeling in urban functional zone mapping and analysis. Additionally, urban functional zone mapping results of Baoshan District, Shanghai, in 2015 and 2020 are presented to verify the effectiveness of the proposed geoscene modeling strategy.

TÜRKİYE’DEKI 81 İL MERKEZİNİN KENT DİJİTALLEŞME ENDEKSLERİNE GÖRE SIRALANMASI

Purpose: The importance of digital Smart City (SC) applications in the productive and effective fulfillment of city services has begun to increase. The aim of this study is calculating the weighted and unweighted urban digitalization indexes (UDI) values and ranking the cities based on these values.Methodology: Indicators, critical success factors (CSF), and dimensions affecting the digital transformation (DT) of cities were determined through literature review, surveys, and interviews. Urban Digitalization Maps (UDM) were prepared and UDI values were calculated with the help of the areas on these maps. The cities have been ranked according to calculated UDI values. In the research, the relevant indicator values were collected from city institutions to measure CSF rates with city data. In these measurements, the Satyam UDI Calculation technique was used to calculate UDIs, and the Categorical Value Selection technique was used to calculate indicator weight-percentages.Findings: In the resulting ranking table, it was seen that each province had different UDIs.UDI rankings and UDMs can enable administrators to take the necessary decisions in determining new SC policies and strategies, and thus use city resources more effectively and productively. As a result, since the DT of cities is not only a technological and temporary transformation, but an intergenerational transformation, it has been proposed to digitalize city services according to the Z-Generation.Originality: This study is the first to measure the level of digital transformation of 81 provinces in Türkiye.

Expropriation between a legal figure and an approach for urban development: global trends and Egyptian reflections

Expropriation of land is considered a crucial instrument of any development practice. The legitimacy of expropriation is essentially reinforced as it is employed for the public interest, often implied as infrastructure and public services. While in the private property on one side and the public interest on the other, urban development acts to be the art of getting the right balance. Despite urban development paradigms significantly affecting expropriation practices, most reviewed literature focused on legal aspects of expropriation. However, few were attentive to addressing the relation between expropriation and the urban universe. What are the current trends of the expropriation function and how extensively is it used for urban development? It can be elaborated as the study’s core question, while the research aims at mapping the development of the expropriation function and how extensively expropriation is used across time and space. Therefore, the study adopts a qualitative comparative approach to explore the global trends of expropriation for urban development and map convergence and deviations. The proposed criteria for comparison aim to discover how extensively expropriation is used, the legal changes and the institutional contexts. In addition, the applied part of the study uses a case study analysis method. A chronological timeline analysis of the Egyptian case was conducted to explore the nature of expropriation practices of Nasser’s time (1956–1970) to Mubarak’s time (1981–2011) in Egypt.Globally, expropriation practices have a heterogeneous nature. Certain features, however, are common enough to justify classifying practices for this study. The research comes up with a schematic mapping intending not to be comprehensive but flexible enough to be applied as a framework to analyse and compare other cases in different contexts. Finally, the research concludes by comparing and positioning the Egyptian case in the light of the global trends and provides principal lessons to planners, governmental parties, and policy-makers.

The Image of Amman City: Structure, Identity, and Meaning

The study provides a better understanding of how city inhabitants perceive and navigate their surroundings hence the urban environment consists of both physical elements and mental representations. The study objective is to review the concept of the image of the city and its implementation in Downtown Amman - Jordan using the descriptive-analytical methodology in addition to site visits and observation that provides a detailed urban mental map for Downtown Amman illustrated by the different layers of the urban fabric. the study shed light on three components of the city image: Identity (the recognition of urban elements as distinct entities), structure (the relationship between urban elements and the observer), and meaning (the relationship between urban elements and the emotional value for the observer). By summarizing the analysis of the downtown Amman image, a visual representation of what appear to be the key issues in the city image. The analysis shows problems such as "Break of continuity", "Branching" and "Lack of integration" have appeared due to the lack of clear organization of the shape of the city facades and the way signs are placed, as well as the mixing of pedestrian paths with vehicles traffic.

Combining SAR images with land cover products for rapid urban flood mapping

Synthetic Aperture Radar (SAR) is an indispensable source of data for mapping and monitoring flood hazards, thanks to its ability to image the Earth’s surface in all weather conditions and at all times. Through cloud computing platforms such as Google Earth Engine (GEE), SAR imagery can be used in near-real time for rapid flood mapping. This has facilitated the disaster response community to make informed decisions in flood hazard interventions and management plans. However, rapid urban flood mapping using SAR is challenging, due to the complex land cover configuration in urban environments, coupled with complicated backscattering mechanisms. Here, we propose a novel method to utilise SAR imagery and land use-land cover (LULC) products for rapid urban flood mapping. Our approach uses a Land Cover Product to segment the study area into LULC types and differentiate each type with respect to whether double bounce is expected to occur during the flooding events. The normalised difference index was derived using a multi-temporal SAR image stack, and the threshold segmentation method was adopted for flood mapping. In addition, DEM and Surface Water datasets were employed to refine the flood extraction results using a morphological correction approach. We assessed the method quantitatively using two use cases: the 2017 Houston and 2022 Coraki flood events. Based on fine spatial resolution optical imagery, the proposed method achieved an accuracy of 92.7% for the August 2017 Houston flood mapping task and 89% for the March 2022 Coraki flood mapping task, which not only represents at least 13% in accuracy compared to non-LCP based flood extraction method, but also provides strong capability for rapid flood mapping in urban settings.

AUTOMATIC BUILDING FOOTPRINT EXTRACTION FROM 3D LASERSCANS

Abstract. Building footprints are a prerequisite for many tasks such as urban mapping and planning. Such structures are mostly derived using airborne laser scanning which reveals rather roof structures than the underlying hidden footprint boundary. This paper introduces an approach to extract a 2D building boundary from a 3D point cloud stemming from either terrestrial scanning or via close-range sensing using a mobile platform, e.g. drone. To this end, a pipeline of methods including non-parametric kernel density estimation (KDE) of an underlying probability density function, a solution of the Travelling Salesperson Problem (TSP), outlier elimination and line segmentation are presented to extract the underlying building footprint. KDE turns out to be suitable to automatically determine a horizontal cut in the point cloud. An ordering of the resulting points in this cut using a shortest possible tour based on TSP allows for the application of existing line segmentation algorithms, otherwise dedicated to indoor segmentation. Outliers in the resulting segments are removed using Density-Based Spatial Clustering of Applications with Noise (DBSCAN). The segments are then generalized leading to the final footprint geometry. We applied our approach on real-world examples and achieved an IoU between 0.930 and 0.998 assessed by ground truth footprints from both authoritative and volunteered geographic information (VGI) data.

Hierarchical Segmentation Method for Generating Road Intersections from Crowdsourced Trajectory Data

Maintaining the data freshness and completeness of road intersection information is the key task of urban road map production and updating. Compared to professional surveying methods, crowdsourced trajectory data provide a low-cost, wide-coverage and real-time data resource for road map construction. However, there may exist the problems of spatio-temporal heterogeneity and uneven density distribution in crowdsourced trajectory data. Hence, in light of road hierarchies, the paper proposes a hierarchical segmentation method to generate road intersections from crowdsourced trajectories. The proposed method firstly implements an adaptive density homogenization processing on raw trajectory data in order to decrease the uneven density discrepancy. Then, a hierarchical segmentation strategy is developed to extract multi-level road intersection elements from coarse scale to fine scale. Finally, the structural models of road intersections are delineated by an iterative piecewise fitting method. Experimental results show that the proposed method can accurately and completely extract road intersections of different shapes and scales, with an accuracy of about 87–90%. Particularly, the precision and recall of road intersection detection are obviously increased by about 7% and 20% by adaptive density homogenization, indicating the advantages of dealing with uneven trajectory data.

Assessment of Urban Mapping Index Accuracy in Relation to Physical Land Characteristics in Humid Tropical Areas

Settlements and built-up areas can lead to the degradation of ecological systems. Good quality and efficient regional planning is therefore needed for urban areas. Spatial data and satellite imagery can be used for mapping and monitoring urban growth. Unfortunately, mapping urban areas can sometimes be difficult due to local variations, and different algorithms can provide varying results. Urban indices often rely on remote sensing reflectance, the accuracy of which can be influenced by land characteristics. No studies have examined the impact of land characteristics on the accuracy of remote sensing urban indices in the humid tropics. The purpose of this study was to compare urban and built area indices, namely EBBI, NDBI, UI, and IBI, in two climatically and topographically different cities. This study also examined the stability and relationship between these indices with environmental factors such as slope, elevation, and temperature. The results showed that EBBI was the index with the highest accuracy in both study areas: 85% for Batu City and 89.17% for Pasuruan City. Also, EBBI was the most stable index for the temporal studies. Environmental factors, especially slope and elevation, had a strong relationship with the index value applied. Therefore, these findings need to be considered in applying the index in areas that have topographical variations.&#x0D; Keywords: built-up land, landsat, EBBI, NDBI, UI, IBI, topography

Estimation of Modal Shift Potential for a New Form of Dial-A-Ride Service

The concept of a dynamic and flexible Intelligent Subscription Bus Service (I-Service) was developed, and two integrated questionnaires were conducted among the commuters of a large university campus. To determine travel times to the campus by I-Service, a digital urban road network map with travel time databases was produced, and software was developed to calculate optimum routes using these databases. Travel times for each participant were determined by the shortest travel time principle. The proposed hypothetical service was introduced to participants, and anticipated advantages for each participant were reported back to them by means of a second questionnaire to determine if they would prefer using I-Service. As a result, a 49% modal shift potential from all other modes in general and a 52% modal shift potential from private car to I-Service were found.