Urban Mapping Research Articles

Urban sensory map: How do tourists “sense” a destination spatially?

Tourism sensory experiences represent a spatially constrained and constructed process influenced by various environmental stimuli. Although growing academic attention has been devoted to sensory tourism, few studies have incorporated spatiality into investigations of sensory experiences. This study establishes a macro–meso–micro analytical framework to explore the relationships among sensory experiences and spatial environmental characteristics based on social media big data. This research also moves beyond the conventional five-sense framework to include a sixth sense—interoception. Results (a) uncover the spatial distribution and relationships among sensory experiences in a destination; (b) demonstrate associations between attraction types and sensory experiences; and (c) illustrate interactions between environmental attributes and sensory encounters. This study theoretically clarifies relevant antecedents, extends a sense-based framework, and multidimensionally enriches tourism sensory experiences; empirically offer guidance for sensory environment planning, marketing, and management. Results also produce methodological insights for adopting social media big data to capture sensory experiences.

CADASTRAL AND URBAN MAPS ENRICHMENTS USING SMART SPATIAL DATA FUSION

Abstract. Cadastral and urban map enrichment/upgrading is an essential requirement for smart urban management. The high pace of development and change in megacities can cause different challenges for urban organizations to reproduce their maps based on their need. New urban management aims and plans need new cadastral and urban maps with different standards and elements which may have existed in the other urban organization. Producing an original map or checking the maps of different organizations visually in a megacity is very costly and time-consuming. These challenges require an advanced integration approach to overcome them. Therefore, enriching maps with concerned organizations' maps and intelligent and automatically identifying as well as applying the changes in urban and cadastral maps will save time and cost for informed urban decision-making. This paper has employed the data of the third zone of the District six of the Municipality of Tehran, the capital of Iran, and identifies changes in the parcel’s geometry of the cadastre maps in comparison with the recently produced maps of the municipality of Tehran. After pre-processing the data, some spatial and attribute information are added to each feature, and the land parcels are enriched. By matching the algorithm and comparing the parcels geometry and attributes, suspicious parcels are identified by the logistic regression algorithm. The Accuracy and F1-Score of this model were 0.845 and 0.780, respectively. Finally, the suspicious parcels are checked and the parcels are located, deleted, merged, splitted and geometrically modified in the base map and the base map is enriched. This paper has successfully proposed a new framework for cadastral and urban map enrichment intelligently.

Bottom-up multilevel flood hazard mapping by integrated inundation modelling in data scarce cities

With increasing urban flood risk due to urbanisation and climate change, flood hazard prediction is ever more crucial for flood risk management and emergency response. However, when sufficient high-quality data are lacking, a standard flood inundation modelling approach has significant uncertainties. Therefore, this study develops a bottom-up approach for urban flood hazard mapping at multiple levels (grid-kilometre-district), built upon the integration of grid-based flood modelling with data acquisition from open sources. This reduces the adverse effects of data scarcity and quality on hazard modelling. In the paper, we first set out an integrated approach for gridded inundation mapping in an urban basin using a hydrodynamic model supported by crowd-sourced social media data. Then, applying the model to flooding in Chengdu in August 2020, we articulate how input data bias and parameter uncertainty both affect urban inundation modelling. The results show that the choice of terrain data and the quantification of urban drainage flows significantly influence the modelled urban inundation extent, depth and duration. This indicates the potential for large variation when using urban inundation mapping merely relying on a single-scale flood hydrodynamic modelling even when supported by crowd-sourced data. The multilevel hazard mapping approach developed here presents multi-layered and comprehensive inundation mapping; thus the effects of data bias or availability are reduced and the coarser hazard mapping shows less sensitivity to the data input quality and model uncertainty, indicating relatively higher reliability at this higher spatial scale. The grid-kilometre-district three level approach provides more reliable flood hazard mapping, which can support rainstorm-induced flood management in data scarce cities.

A Machine Learning Framework for Assessing Urban Growth of Cities and Suitability Analysis

Rural–urban immigration, regional wars, refugees, and natural disasters all bring to prominence the importance of studying urban growth. Increased urban growth rates are becoming a global phenomenon creating stress on agricultural land, spreading pollution, accelerating global warming, and increasing water run-off, which adds exponentially to pressure on natural resources and impacts climate change. Based on the integration of machine learning (ML) and geographic information system (GIS), we employed a framework to delineate future urban boundaries for future expansion and urban agglomerations. We developed it based on a Time Delay Neural Network (TDNN) that depends on equal time intervals of urban growth. Such an approach is used for the first time in urban growth as a predictive tool and is coupled with Land Suitability Analysis, which incorporates both qualitative and quantitative data to propose evaluated urban growth in the Greater Irbid Municipality, Jordan. The results show the recommended future spatial expansion and proposed results for the year 2025. The results show that urban growth is more prevalent in the eastern, northern, and southern areas and less in the west. The urban growth boundary map illustrates that the continuation of urban growth in these areas will slowly further encroach upon and diminish agricultural land. By means of suitability analysis, the results showed that 51% of the region is unsuitable for growth, 43% is moderately suitable and only 6% is suitable for growth. Based on TDNN methodology, which is an ML framework that is dependent on the growth of urban boundaries, we can track and predict the trend of urban spatial expansion and thus develop policies for protecting ecological and agricultural lands and optimizing and directing urban growth.

Supervised versus Semi-Supervised Urban Functional Area Prediction: Uncertainty, Robustness and Sensitivity

To characterize a community-scale urban functional area using geo-tagged data and available land-use information, several supervised and semi-supervised models are presented and evaluated in Hong Kong for comparing their uncertainty, robustness and sensitivity. The following results are noted: (i) As the training set size grows, models’ accuracies are improved, particularly for multi-layer perceptron (MLP) or random forest (RF). The graph convolutional network (GCN) (MLP or RF) model reveals top accuracy when the proportion of training samples is less (greater) than 10% of the total number of functional areas; (ii) With a large amount of training samples, MLP shows the highest prediction accuracy and good performances in cross-validation, but less stability on same training sets; (iii) With a small amount of training samples, GCN provides viable results, by incorporating the auxiliary information provided by the proposed semantic linkages, which is meaningful in real-world predictions; (iv) When the training samples are less than 10%, one should be cautious using MLP to test the optimal epoch for obtaining the best accuracy, due to its model overfitting problem. The above insights could support efficient and scalable urban functional area mapping, even with insufficient land-use information (e.g., covering only ~20% of Beijing in the case study).

Multi-agent hyperspectral and lidar features fusion for urban vegetation mapping

Multi-agent hyperspectral and lidar features fusion for urban vegetation mapping

Urban Heritage Mapping: GIS Applications based Archaeological Explorations of Earliest Medieval Cities of Delhi

Urban Heritage Mapping: GIS Applications based Archaeological Explorations of Earliest Medieval Cities of Delhi

Monitoring Urban Expansion by Coupling Multi-Temporal Active Remote Sensing and Landscape Analysis: Changes in the Metropolitan Area of Cordoba (Argentina) from 2010 to 2021

Uncontrolled and unsustainable urban sprawl are altering the Earth’s surface at unprecedented rates. This research explores the potential of active remote sensors for mapping urban areas, for monitoring urban expansion processes and for depicting landscape pattern dynamics in a metropolis of South America. Based on multi-temporal urban cover maps of Cordoba, Argentina, purposely derived from COSMO-SkyMed SAR data by urban extraction algorithms, we quantified urban surface increase and described urbanization processes that occurred during 2010–2021 in sectors with different degrees of soil sealing. We extracted urban extent in four time-steps using an Urban EXTent extraction (UEXT) algorithm and quantified urban expansion, identifying newly built areas on 2.5 ha cells. For these cells, we computed urban cover and a set of landscape pattern indices (PIs), and by projecting them in a composition vs. configuration Cartesian space we performed a trajectory analysis. SAR-based urban extraction and cover change proved to be very accurate. Overall accuracy and Cohen’s Kappa statistic evidenced very high values, always above 91.58% and 0.82, respectively, for urban extraction, and also above 90.50% and 0.72 concerning the accuracy of urban expansion. Cordoba’s urban surface significantly increased (≈900 ha in 10 years) following three main spatial processes in different city sectors (e.g., edge-expansion and outlying on peri-urban areas, and infill inside the ring road), which may have contrasting effects on the sustainability of the metropolitan area. Trajectory analysis highlighted non-linear relations between the urban cover and the PIs. Areas with very low and low urban intensity underwent a steep rise of both urban cover and PI values (e.g., urban patch dimension, complexity and number), depicting urban edge-expansion and outlying processes. In the areas with medium and high urban intensity the increase in patch dimension, along with the decrease in patch number and complexity, evidence the coalescence of urban areas that incorporate in the urban fabric the remnants of non-built up zones and fill the few residual green spaces. The proposed SAR mapping procedure coupled with landscape analysis proved to be useful to detect and depict different moments of urban expansion and, pending more tests on other cities and geographical conditions, it could be postulated among the RS indicators to monitor the achievement of the Sustainable Development Goals established by the United Nations.

Mapping and evaluating global urban entities (2000–2020): A novel perspective to delineate urban entities based on consistent nighttime light data

ABSTRACT The differences in the definition of urban areas lead to our contrasting or inconsistent understanding of global urban development and their corresponding socioeconomic and environmental impacts. The existing urban areas were widely identified by the boundaries of built-environment or social-connections, rather than urban entities that are essentially the spatial extents of human activity agglomerations. Thus, this study has attempted to map and evaluate global urban entities (2000–2020) from a perspective of an updated urban concept of urban entities based on the consistent remotely sensed nighttime light data. First, a K-means algorithm was developed to cluster urban and non-urban pixels automatically in consideration of global region division. Then, a post-processing was conducted to enhance the temporal and logical consistency of urban entities during the study period. Rationality assessment indicates that urban entities derived from remotely sensed nighttime light data more effectively reflect the spatial agglomeration extents of human activities than those of physical urban areas. Global urban entities increased from 157,733 km2 in 2000 to 470,632 km2 in 2020 accompanied by a differentiated urban expansion at global, continental, and national levels. Our study provides long-time series and fine-resolution datasets (500 m) and new research avenues for spatiotemporal analysis of global urban entity expansion with the improvement of the understanding of urbanization and the emergence of effective urban mapping theories and approaches.

Review of Urban Cultural Map Research Based on “Mapping”

Urban cultural maps are important analytical tools that can guide urban design and planning from the perspective of information, provide services for urban cultural development, promote cultural planning and the construction of urban space, and reflect and promote the process of urban cultural progress in a dynamic way. Mapping is often used as a process tool for mapping, cognition, and analysis in geography or surveying and has recently been applied in architectural science. The field of architecture has given mapping a new definition and played a certain role in urban research and development. This article introduces the concepts of mapping and urban cultural maps, as well as the relationship between them. Through a review of theoretical research and relevant practices, it provides new perspectives and possibilities for urban development.

Forest Mapping Using a VGG16-UNet++& Stacking Model Based on Google Earth Engine in the Urban Area

Accurate, detailed urban forest mapping contributes to ecological status monitoring and formulating sustainable development policies in cities worldwide. However, accurate urban forest identification in southern Chinese cities is challenging when samples are insufficient because of high fragmentation and the influence of mountain shadows and cloudy weather. Therefore, this study combined the advantages of transfer, deep, and ensemble learning to propose a VGG16-UNet++&Stacking algorithm for urban forest mapping in heavily urbanized areas based on the Sentinel dataset. Initially, the algorithm mined deep features of an image by pre-training the convolutional layer. Then, the deep feature set was fed into ensemble learning for classification to improve accuracy and robustness. The classification results showed that the VGG16-UNet++&Stacking had an overall accuracy (OA) of 95.87% and a Kappa coefficient of 0.9481. Furthermore, the user accuracy of the forest was 97.94%. The OA of the method in this study was improved by 2.2% and 3.83% compared with that of UNet++ and random forest (RF), respectively. Compared to that of UNet++, the results showed a modest improvement in OA for the VGG16-UNet++&Stacking method; VGG16-UNet++&Stacking is more effective in eliminating cloud influence, a feature that UNet++ lacks.

UAV-Based P-Band SAR Tomography With Long Baseline: A Multimaster Approach

Due to the advantage of flexible and rapid deployment, unmanned aerial vehicle (UAV)-based synthetic aperture radar (SAR) tomography (TomoSAR) is a promising technology in 3-D urban mapping. The long baseline is indispensable for P-band SAR systems to achieve high elevation resolution. It will introduce two problems. On the one hand, the unavoidable spatial decorrelation brings serious phase noise and sidelobes in 3-D imaging. On the other hand, the noticeable image distortion fails the image registration and the TomoSAR data stack (TDS) construction. Aiming at the above problems, this article proposes a multimaster (MM) TomoSAR approach via three main contributions. First, the traditional TomoSAR signal model is extended to the MM case to improve the number of baselines and the average image coherence of the TDS and suppress the sidelobes. Second, a short-baseline-recursion image registration method is proposed to achieve high-precision image registration. Third, a TDS optimization processing consisting of interferometric SAR (InSAR) phase screening and baseline sign reassignment is introduced. Moreover, a clustering-based outliers’ elimination method is also adopted to ensure the 3-D imaging quality. Computer simulation and long-baseline P-band UAV-SAR experiment validate the proposed approach.

A Subpixel Mapping Method for Urban Land Use by Reducing Shadow Effects

Urban land use classification is significant for urban development planning. Considering complex environments of urban surface features, traditional semantic segmentation methods are difficult to solve the problems of mixed pixels and limited spatial resolution of images. The subpixel mapping technology is an effective method to solve the above problems in urban land use classification. However, traditional subpixel mapping methods are sensitive to mountain shadow, high-rise building shadow and impermeable surface heterogeneity, resulting in false classification. Therefore, we propose a subpixel mapping method that can reduce the shadow effect. This method uses a multi-index feature fusion strategy to optimize the abundance of the shadow errors in the abundance image, and uses a super-resolution reconstruction neural network model to reconstruct the optimized abundance image for the subpixel mapping of urban land use. Experiments were conducted on sentinel-2 images obtained over Yuelu District of Changsha City, Hunan Province, China. The experimental results show that the method proposed in this article can effectively overcome the influence of building shadows and mountain shadows in urban land cover classification and is superior to traditional subpixel/pixel spatial attraction model, radial basis function, super-resolution subpixel mapping, and other methods in the effect and accuracy of urban land use subpixel mapping.

Urban Renewal Mapping: A Case Study in Beijing from 2000 to 2020

Understanding the distribution and land history of old urban areas (OUAs) and renewed urban areas (RUAs) has become the key point of urban management. However, it is hard to acquire adequate information for lack of pertinent detection methods. Here, we established a complete mapping framework on Google Earth Engine (GEE) platform to identify OUAs and RUAs and detect the temporal information of urban renewal, which was implemented in Beijing during 2000–2020. We used Landsat imagery and LandTrendr algorithm to fit the spectral trajectories of 14 bands/indices with specific segment attributes as the feature inputs for Random Forest classification. We produced the maps of OUAs and RUAs with an overall accuracy of 95.36%. On this basis, we further utilized LandTrendr to detect the start year, end year, and duration of urban renewal with the accuracies within the ±5-year difference of 85.52%, 80.97%, and 74.53%, respectively. These maps all present informative spatiotemporal patterns. Furthermore, the urban renewal process is likely to be influenced by major national or international events. The study answers the issues about urban renewal from multiple angles and provides scientific support for future urban planning.

Exploring the Addition of Airborne Lidar-DEM and Derived TPI for Urban Land Cover and Land Use Classification and Mapping

The classification and mapping accuracy of urban land cover and land use has always been a critical topic and several auxiliary data have been used to improve the classification accuracy. However, to the best of our knowledge, there is limited knowledge of the addition of airborne Light Detection and Ranging (lidar)-Digital Elevation Model (DEM) and Topographic Position Index (TPI) for urban land cover and land use classification and mapping. The aim of this study was to explore the addition of airborne lidar-DEM and derived TPI to reflect data of Landsat Operational Land Imager (OLI) in improving the classification accuracy of urban land cover and land use map- ping. Specifically, this study explored the mapping accuracies of urban land cover and land use classifications derived using: 1) standalone Landsat OLI satellite data; 2) Landsat OLI with acquired airborne lidar-DEM ; 3) Landsat OLI with TPI ; and 4) Landsat OLI with airborne lidar-DEM and derived TPI. The results showed that the addition of airborne lidar-DEM and TPI yielded the best overall urban land cover and land use classification accuracy of about 88%. The findings in this study demonstrated that both lidar-DEM and TPI had a positive impact in improving urban land cover and land use classification.

An Individual Tree Segmentation Method From Mobile Mapping Point Clouds Based on Improved 3-D Morphological Analysis

Street tree extraction based on the 3D mobile mapping point cloud plays an important role in building smart cities and creating highly accurate urban street maps. Existing methods are often over- or under-segmented when segmenting overlapping street tree canopies and extracting geometrically complex trees. To address this problem, we propose a method based on improved 3D morphological analysis for extracting street trees from mobile laser scanner (MLS) point clouds. First, the 3D semantic point cloud segmentation framework based on Deep Learning is used for pre-classification of the original point cloud to obtain the vegetation point cloud in the scene. Considering the influence of terrain unevenness, the vegetation point cloud is de-terraformed and slice point cloud containing tree trunks is obtained through spatial filtering on height. On this basis, a voxel-based region growing method constrained with the changing rate of convex area is used to locate the stree trees. Then we propose an progressive tree crown segmentation method, which first completed the preliminary individual segmentation of the tree crown point cloud based on the voxel-based region growth constrained by the minimum increment rule, and then optimizes the crown edges by “valley” structure-based clustering. In this paper, the proposed method is validated and the accuracy is evaluated using three sets of MLS Datasets collected from different scenarios. The experimental results show that the method can effectively identify and localize street trees with different geometries and has a good segmentation effect for street trees with large adhesion between canopies. The accuracy and recall of tree localization are higher than 96.08% and 95.83%, respectively, and the average precision and recall of instance segmentation in three datasets are higher than 93.23% and 95.41%, respectively.

Innovative solutions in creating noise maps in cities

This article describes the procedure for performing geodetic and cartographic mapping of urban noise maps. Modern geodetic (shumometric) devices used to measure noise frequencies, and the coordinates of the limit of noise impact on urban buildings and structures using technology and processing of measured data using modern geodetic software (Panorama). With the help of the panorama program, noise maps of the noisy streets of Samarkand were created based on the legend. The scientific significance of the results of the study lies in the fact that the geodetic bases are created using satellites, that is, instead of the bases created along the road, sound level meters are installed and noise measurements are taken. This is due to the fact that a method has been developed for creating noise maps using programs and technologies based on measured noise propagation data, as a result of which a method has been developed for describing noise propagation with cartographic symbols and it has become possible to use them on a global scale. The practical significance of the research results lies in the fact that the PANORAMA program created modern information and relevant opportunities based on space data, developed noise maps of the streets of Samarkand, M. Ulugbek, A. Rudakiy, A. Beruniy, Registan, and on these maps it can be seen that the decibels of noise in the city are increasing every year, determined by the fact that it will serve as an important mapping resource for the future.

MSRF-Net: Multiscale Receptive Field Network for Building Detection From Remote Sensing Images

Extracting buildings from remote sensing images plays an important role in urban development planning, disaster assessment and mapping. Convolutional neural network (CNN) has been widely applied to building extraction because of its powerful deep semantic feature extraction ability. However, existing CNN-based building extraction methods are difficult to accurately extract multiscale buildings with accurate edges because of the limitation of feature receptive fields and the loss of spatial detail information. For the above problems, this paper proposes a multiscale receptive field network (MSRF-Net) to accurately extract multiscale buildings from remote sensing images. MSRF-Net includes multiscale receptive field feature encoder (MRFF-Encoder) and multipath decoder. In the MRFF-Encoder, a multiscale attentional down (MSAD) module and asymmetric residual inception (ARI) module are proposed to capture multiscale receptive field features. In the multipath decoder, convolutions with different kernel size and dilation are used in three parallel paths to learn localization-preserved multiscale features with multiscale receptive field. What’s more, the features of different branches and MRFF-Encoder are fused by the proposed feature combination module, which contribute to capture context information of multiscale receptive field while recovering the resolution of feature space. The experimental results show that compared with the latest MAP-Net, MSRF-Net has achieved F1 score growth of 1.14%, 0.42%, 1.11% and IoU score growth of 1.68%, 0.76% and 1.64% respectively on Massachusetts data set, WHU data set and the Typical Cities Building data set.

A Shape and Size Free-CNN for Urban Functional Zone Mapping With High-Resolution Satellite Images and POI Data

A Shape and Size Free-CNN for Urban Functional Zone Mapping With High-Resolution Satellite Images and POI Data

Array 3-D SAR Tomography Using Robust Gridless Compressed Sensing

Tomographic synthetic aperture radar (TomoSAR), which can provide three-dimensional (3-D) image of the observed scenes, has become an important technology for topographic mapping, forest parameter estimation, urban buildings modeling and etc. Recently, the developed compressed sensing (CS) and other similar methods have been widely applied for the achievement of super-resolution SAR tomography. However, there always exists inevitable model errors during the mining of scene information, such as discrete gridding on used dictionary and outliers among independent identically distribution (IID) samples, which tends to dramatically degrade the TomoSAR inversion. In this paper, a novel robust gridless CS (RGLCS) algorithm is proposed for high-resolution 3-D imaging of array TomoSAR. In the scheme, the atomic norm minimization (ANM) is used to model the joint-sparsity pattern on elevation distribution between adjacent pixels, which can be treated as gridless CS to avoid the discrete error of the dictionary. Meanwhile, the outliers and disturbances not satisfying the IID elevation distribution are modelled as sparsely distributed spike-noise in the image domain. The proposed RGLCS algorithm has the capability of perfectly separating the outliers and maintaining high-precision height resolution. For efficient solution, a fast alternative optimization is used to solve the objective function to effectively reduce the computational complexity. Next, the post-processing, including point cloud clustering and double-bounce scattering detection & eliminating, are studied to obtain high-resolution 3-D point cloud image. Finally, the experimental analysis using both simulated and measured data are performed to verify the effectiveness of the proposed algorithm. In particular, a practical demonstration using measured airborne array TomoSAR data is presented for urban mapping.