remotesensing-logo

Journal Browser

Journal Browser

Special Issue "Remote Sensing-Based Urban Planning Indicators"

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Urban Remote Sensing".

Deadline for manuscript submissions: closed (30 September 2020) | Viewed by 32263

Special Issue Editors

Dr. Monika Kuffer
E-Mail Website
Guest Editor
Faculty of Geo-Information Science and Earth Observation (ITC) of the University of Twente, 7514 AE Enschede, The Netherlands
Interests: urban remote sensing; urban modelling; spatial statistics; urban planning; slum mapping; deprived area mapping
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Karin Pfeffer
E-Mail Website
Guest Editor
Full Professor, Faculty of Geo-Information Science and Earth Observation (ITC) of the University of Twente, Department of Urban and Regional Planning and Geo-information Management, PO Box 217, 7500 AE Enschede, The Netherlands
Interests: urban studies; urbanization; urban deprivations; spatial inequality; quality of life; urban vulnerabilities; urban patterns; urban governance; urban infrastructures
Special Issues, Collections and Topics in MDPI journals
Dr. Claudio Persello
E-Mail Website
Guest Editor
Faculty of Geo-Information Science and Earth Observation (ITC) of the University of Twente, Department of Earth Observation Science, PO Box 217, 7500 AE Enschede, The Netherlands
Interests: remote sensing; machine learning; deep learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Urban Planning deals with complex tasks that have to be carried out within a multi-disciplinary and multi-stakeholder environment. More specifically, it is concerned with the regulation of land uses, the built environment and the development of urban areas including housing, infrastructure and urban services. Urban Planning also needs to balance competing interests between economic development, urban environmental conditions and social needs and design strategies for short- and longterm developments as well as sudden events and shocks.

Strategic urban development focuses on numerous normative goals aspiring to develop sustainable, inclusive, competitive, resilient and compact cities and satisfy the needs of different groups (e.g., age, gender, income and cultural groups). In a typical planning process, planners explore, analyze, design, implement and assess the outcome of urban development plans and strategies. These planning activities (exploring - analyzing - designing – implementing – evaluating - monitoring), iterative in nature, require relevant tools, instruments and information to analyze and assess potential outcomes, both in terms of benefits and negative effects. Tools are for instance well-designed indicators to measure and monitor the state of urban aspects such as the rate of land consumption, land use patterns, environmental conditions and exposure, public health, access to services or urban deprivation, as they allow to understand and discuss the complexity of the urban development process, key to urban planning and decision making.

In particular, rapidly developing cities in resource constraint environments face considerable challenges to support urban planning processes at various scales with well-designed indicators as well as adequate data to measure these indicators (e.g., ranging from a detailed/local level to metropolitan level indicators). However, such indicators and data are important for urban planning and decision making and the effective communication within a multi-stakeholder environment. Given the high spatial and temporal resolution and coverage, remote sensing can supply relevant base data to support the development, measuring and monitoring of urban indicators at different scales. Remote sensing not only provides 2D, but also 3D information that allows to analyze vertical conditions. Furthermore, remote sensing data cover historic time spans to analyze temporal dynamics and therefore support simulations and scenario building. Moreover, remote sensing data are becoming more widely available and accessible.

This special issue seeks contributions to create a broad overview on the scope of urban remote sensing data, methods and applications in support of urban planning indicators, both in the Global North and South. 

Contributions include, but are not limited to, the following:

  • Urban land consumption rates, open spaces, green spaces, built-up densities and their temporal dynamics, e.g., supporting urban green and open space planning.
  • Urban growth and land use patterns and changes and the interaction with urban infrastructure provision, e.g., supporting urban land use planning.
  • Urban population modelling, e.g. urban population densities and dasymetric modelling.
  • Urban 3D and 4D modelling using data of various scales, e.g., supporting urban design and scenario development.
  • Urban environmental issues (climate, air, water and land) and their dynamics at various urban scales, e.g., supporting urban environmental planning.  
  • Urban infrastructure and urban services and their interaction with the general urban and land use development, e.g., supporting urban upgrading projects.
  • Urban exposure, vulnerability, resilience and sustainability, including urban hazards and risks, e.g., supporting disaster risk reduction.
  • Urban social and economic aspect, e.g. inclusion and deprivation, e.g., the role of remote sensing to provide social and economic indicators for urban development plans.
  • Urban health and their linkage with urban environmental exposure, urban patterns, population distribution and dynamics, e.g., supporting urban public health planning.
  • Critical contributions with regard to measuring indicators of the Sustainable Development Goal 11 or the Sendai Framework.

Dr. Monika Kuffer
Prof. Dr. Karin Pfeffer
Dr. Claudio Persello
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2500 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • urban planning
  • urban indicators
  • urban environment
  • social and economic indicators
  • urban growth
  • urban dynamics
  • urban land use
  • structure types
  • urban hazards

Published Papers (14 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Editorial

Jump to: Research, Other

Editorial
Special Issue “Remote-Sensing-Based Urban Planning Indicators”
Remote Sens. 2021, 13(7), 1264; https://doi.org/10.3390/rs13071264 - 26 Mar 2021
Viewed by 1153
Abstract
We are living in an urban age [...] Full article
(This article belongs to the Special Issue Remote Sensing-Based Urban Planning Indicators)
Show Figures

Graphical abstract

Research

Jump to: Editorial, Other

Article
Analysing Urban Development Patterns in a Conflict Zone: A Case Study of Kabul
Remote Sens. 2020, 12(21), 3662; https://doi.org/10.3390/rs12213662 - 08 Nov 2020
Cited by 2 | Viewed by 2065
Abstract
A large part of the population in low-income countries (LICs) lives in fragile and conflict-affected states. Many cities in these states show high growth dynamics, but little is known about the relation of conflicts and urban growth. In Afghanistan, the Taliban regime, which [...] Read more.
A large part of the population in low-income countries (LICs) lives in fragile and conflict-affected states. Many cities in these states show high growth dynamics, but little is known about the relation of conflicts and urban growth. In Afghanistan, the Taliban regime, which lasted from 1996 to 2001, caused large scale displacement of the population. People from Afghanistan migrated to neighboring countries like Iran and Pakistan, and all developments came to a halt. After the US invasion in October 2001, all the major cities in Afghanistan experienced significant population growth, in particular, driven by the influx of internally displaced persons. Maximum pressure of this influx was felt by the capital city, Kabul. This rapid urbanization, combined with very limited capacity of local authorities to deal with this growth, led to unplanned urbanization and challenges for urban planning and management. This study analyses the patterns of growth between 2001 and 2017, and the factors influencing the growth in the city of Kabul with the help of high-resolution Earth Observation-based data (EO) and spatial logistic regression modelling. We analyze settlement patterns by extracting image features from high-resolution images (aerial photographs of 2017) and terrain features as input to a random forest classifier. The urban growth is analyzed using an available built-up map (extracted from IKONOS images for the year 2001). Results indicate that unplanned settlements have grown 4.5 times during this period, whereas planned settlements have grown only 1.25 times. The unplanned settlements expanded mostly towards the west and north west parts of the city, and the growth of planned settlements happened mainly in the central and eastern parts of the city. Population density and the locations of military bases are the most important factors that influence the growth, of both planned and unplanned settlements. The growth of unplanned settlement occurs predominantly in areas of steeper slopes on the hillside, while planned settlements are on gentle slopes and closer to the institutional areas (central and eastern parts of the city). We conclude that security and availability of infrastructure were the main drivers of growth for planned settlements, whereas unplanned growth, mainly on hillsides, was driven by the availability of land with poor infrastructure. Full article
(This article belongs to the Special Issue Remote Sensing-Based Urban Planning Indicators)
Show Figures

Graphical abstract

Article
Investigating Seasonal Effects of Dominant Driving Factors on Urban Land Surface Temperature in a Snow-Climate City in China
Remote Sens. 2020, 12(18), 3006; https://doi.org/10.3390/rs12183006 - 15 Sep 2020
Cited by 4 | Viewed by 1113
Abstract
Land surface temperature (LST) is a crucial parameter in surface urban heat island (SUHI) studies. A better understanding of the driving mechanisms, influencing variations in LST dynamics, is required for the sustainable development of a city. This study used Changchun, a city in [...] Read more.
Land surface temperature (LST) is a crucial parameter in surface urban heat island (SUHI) studies. A better understanding of the driving mechanisms, influencing variations in LST dynamics, is required for the sustainable development of a city. This study used Changchun, a city in northeast China, as an example, to investigate the seasonal effects of different dominant driving factors on the spatial patterns of LST. Twelve Landsat 8 images were used to retrieve monthly LST, to characterize the urban thermal environment, and spectral mixture analysis was employed to estimate the effect of the driving factors, and correlation and linear regression analyses were used to explore their relationships. Results indicate that, (1) the spatial pattern of LST has dramatic monthly and seasonal changes. August has the highest mean LST of 38.11 °C, whereas December has the lowest (−19.12 °C). The ranking of SUHI intensity is as follows: summer (4.89 °C) > winter with snow cover (1.94 °C) > spring (1.16 °C) > autumn (0.89 °C) > winter without snow cover (−1.24 °C). (2) The effects of driving factors also have seasonal variations. The proportion of impervious surface area (ISA) in summer (49.01%) is slightly lower than those in spring (56.64%) and autumn (50.85%). Almost half of the area is covered with snow (43.48%) in winter. (3) The dominant factors are quite different for different seasons. LST possesses a positive relationship with ISA for all seasons and has the highest Pearson coefficient for summer (r = 0.89). For winter, the effect of vegetation on LST is not obvious, and snow becomes the dominant driving factor. Despite its small area proportion, water has the strongest cooling effect from spring to autumn, and has a warming effect in winter. (4) Human activities, such as agricultural burning, harvest, and different choices of crop species, could also affect the spatial patterns of LST. Full article
(This article belongs to the Special Issue Remote Sensing-Based Urban Planning Indicators)
Show Figures

Figure 1

Article
Classification of Urban Area Using Multispectral Indices for Urban Planning
Remote Sens. 2020, 12(15), 2503; https://doi.org/10.3390/rs12152503 - 04 Aug 2020
Cited by 6 | Viewed by 2552
Abstract
An accelerating trend of global urbanization accompanying population growth makes frequently updated land use and land cover (LULC) maps critical. LULC maps have been widely created through the classification of remotely sensed imagery. Maps of urban areas have been both dichotomous (urban or [...] Read more.
An accelerating trend of global urbanization accompanying population growth makes frequently updated land use and land cover (LULC) maps critical. LULC maps have been widely created through the classification of remotely sensed imagery. Maps of urban areas have been both dichotomous (urban or non-urban) and entailing of discrete urban types. This study incorporated multispectral built-up indices, designed to enhance satellite imagery, for introducing new urban classification schemes. The indices examined are the new built-up index (NBI), the built-up area extraction index (BAEI), and the normalized difference concrete condition index (NDCCI). Landsat Level-2 data covering the city of Miami, FL, USA was leveraged with geographic data from the Florida Geospatial Data Library and Florida Department of Environmental Protection to develop and validate new methods of supervised and unsupervised classification of urban area. NBI was used to extract discrete urban features through object-oriented image analysis. BAEI was found to possess properties for visualizing and tracking urban development as a low-high gradient. NDCCI was composited with NBI and BAEI as the basis for a robust urban intensity classification scheme superior to that of the United States Geological Survey National Land Cover Database 2016. BAEI, implemented as a shadow index, was incorporated in a novel infill geosimulation of high-rise construction. The findings suggest that the proposed classification schemes are advantageous to the process of creating more detailed cartography in response to the increasing global demand. Full article
(This article belongs to the Special Issue Remote Sensing-Based Urban Planning Indicators)
Show Figures

Graphical abstract

Article
Misperceptions of Predominant Slum Locations? Spatial Analysis of Slum Locations in Terms of Topography Based on Earth Observation Data
Remote Sens. 2020, 12(15), 2474; https://doi.org/10.3390/rs12152474 - 01 Aug 2020
Cited by 8 | Viewed by 1777
Abstract
Slums are a physical expression of poverty and inequality in cities. According to the UN definition, this inequality is, e.g., reflected in the fact that slums are much more often located in hazardous zones. However, this has not yet been empirically investigated. In [...] Read more.
Slums are a physical expression of poverty and inequality in cities. According to the UN definition, this inequality is, e.g., reflected in the fact that slums are much more often located in hazardous zones. However, this has not yet been empirically investigated. In this study, we derive proxies from multi-sensoral high resolution remote sensing data to investigate both the location of slums and the location of slopes. We do so for seven cities on three continents. Using a chi-squared test of homogeneity, we compare the locations of formal areas with that of slums. Contrary to the perception indirectly stated in the literature, we find that slums are in none of the sample cities predominantly located in these exposed areas. In five out of seven cities, the spatial share of slums on hills steeper than 10° is even less than 5% of all slums. However, we also find a higher likelihood of slums occurring in these exposed areas than of formal settlements. In six out of seven sample cities, the probability that a slum is located in steep areas is higher than for a formal settlement. As slums mostly feature higher population densities, these findings reveal a clear tendency that slum residents are more likely to settle in exposed areas. Full article
(This article belongs to the Special Issue Remote Sensing-Based Urban Planning Indicators)
Show Figures

Graphical abstract

Article
Prediction of Socio-Economic Indicators for Urban Planning Using VHR Satellite Imagery and Spatial Analysis
Remote Sens. 2020, 12(11), 1730; https://doi.org/10.3390/rs12111730 - 28 May 2020
Cited by 9 | Viewed by 1861
Abstract
Ongoing urbanization leads to steady growth of urban areas. In the case of highly dynamic change of municipalities, due to the rates of change, responsible administrations often are challenged or struggle with capturing present states of urban sites or accurately planning future urban [...] Read more.
Ongoing urbanization leads to steady growth of urban areas. In the case of highly dynamic change of municipalities, due to the rates of change, responsible administrations often are challenged or struggle with capturing present states of urban sites or accurately planning future urban development. An interest for urban planning lies on socio-economic conditions, as consumption and production of disposable goods are related to economic possibilities. Therefore, we developed an approach to generate relevant parameters for infrastructure planning by means of remote sensing and spatial analysis. In this study, the single building defines the spatial unit for the parameters. In the case city Belmopan (Belize), based on WorldView-1 data we manually define a city covering building dataset. Residential buildings are classified to eight building types which are locally adapted to Belmopan. A random forest (RF) classifier is trained with locally collected training data. Through household interviews focusing on household assets, income and educational level, a socio-economic point (SEP) scaling is defined, which correlates very well with the defined building typology. In order to assign socio-economic parameters to the single building, five socio-economic classes (SEC) are established based on SEP statistics for the building types. The RF building type classification resulted in high accuracies. Focusing on the three categories to describe residential socio-economic states allowed high correlations between the defined building and socio-economic points. Based on the SEP we projected a citywide residential socio-economic building classification to support supply and disposal infrastructure planning. Full article
(This article belongs to the Special Issue Remote Sensing-Based Urban Planning Indicators)
Show Figures

Graphical abstract

Article
Assessment of Urban Dynamics to Understand Spatiotemporal Differentiation at Various Scales Using Remote Sensing and Geospatial Tools
Remote Sens. 2020, 12(8), 1306; https://doi.org/10.3390/rs12081306 - 21 Apr 2020
Cited by 8 | Viewed by 1767
Abstract
Analysis of urban dynamics is a pivotal step towards understanding landscape changes and developing scientifically sound urban management strategies. Delineating the patterns and processes shaping the evolution of urban regions is an essential part of this step. Utilizing remote-sensing techniques and Geographic Information [...] Read more.
Analysis of urban dynamics is a pivotal step towards understanding landscape changes and developing scientifically sound urban management strategies. Delineating the patterns and processes shaping the evolution of urban regions is an essential part of this step. Utilizing remote-sensing techniques and Geographic Information System (GIS) tools, we performed an integrated analysis on urban expansion in Srinagar city and surrounding areas from 1999 to 2017 at multiple scales in order to assist urban planning initiatives. To capture various spatial indicators of expansion, we analysed (i) land use/land cover (LULC) changes, (ii) rate and intensity of changes to built-up areas, (iii) spatial differentiation in landscape metrics (at 500, 1000 and 2000 m cell-size), and (iv) growth type of the urban expansion. Global Moran’s I statistics and local indicators of spatial association (LISA) were also employed to identify hotspots of change in landscape structure. Our methodology utilizes a range of geovisualization tools which are capable of appropriately addressing various elements required for strategic planning in growing cities. The results highlight aggregation and homogenization of the urban core as well as irregularity and fragmentation in its periphery. A combination of spatial metrics and growth type analysis supports the supposition that there is a continuum in the diffusion-coalescence process. This allows us to extend our understanding of urban growth theory and to report deviations from accepted stages of growth. As our results show, each dominating growth phase of the city—both diffusion (1999) and coalescence (2009 and 2017)—is interspersed with features from the other type. An improved understanding of spatial differentiation and the identification of hotspots can serve to make urban planning more tailored to such local conditions. An important insight derived from the results is the applicability of remote-sensing data in urban planning measures and the usefulness of freely available medium resolution data in gaining a comprehensive understanding of the evolution of cities. Full article
(This article belongs to the Special Issue Remote Sensing-Based Urban Planning Indicators)
Show Figures

Graphical abstract

Article
Cloud Computation Using High-Resolution Images for Improving the SDG Indicator on Open Spaces
Remote Sens. 2020, 12(7), 1144; https://doi.org/10.3390/rs12071144 - 03 Apr 2020
Cited by 6 | Viewed by 2695
Abstract
Open spaces are essential for promoting quality of life in cities. However, accelerated urban growth, in particular in cities of the global South, is reducing the often already limited amount of open spaces with access to citizens. The importance of open spaces is [...] Read more.
Open spaces are essential for promoting quality of life in cities. However, accelerated urban growth, in particular in cities of the global South, is reducing the often already limited amount of open spaces with access to citizens. The importance of open spaces is promoted by SDG indicator 11.7.1; however, data on this indicator are not readily available, neither globally nor at the metropolitan scale in support of local planning, health and environmental policies. Existing global datasets on built-up areas omit many open spaces due to the coarse spatial resolution of input imagery. Our study presents a novel cloud computation-based method to map open spaces by accessing the multi-temporal high-resolution imagery repository of Planet. We illustrate the benefits of our proposed method for mapping the dynamics and spatial patterns of open spaces for the city of Kampala, Uganda, achieving a classification accuracy of up to 88% for classes used by the Global Human Settlement Layer (GHSL). Results show that open spaces in the Kampala metropolitan area are continuously decreasing, resulting in a loss of open space per capita of approximately 125 m2 within eight years. Full article
(This article belongs to the Special Issue Remote Sensing-Based Urban Planning Indicators)
Show Figures

Graphical abstract

Article
The Temporal Dynamics of Slums Employing a CNN-Based Change Detection Approach
Remote Sens. 2019, 11(23), 2844; https://doi.org/10.3390/rs11232844 - 29 Nov 2019
Cited by 24 | Viewed by 2437
Abstract
Along with rapid urbanization, the growth and persistence of slums is a global challenge. While remote sensing imagery is increasingly used for producing slum maps, only a few studies have analyzed their temporal dynamics. This study explores the potential of fully convolutional networks [...] Read more.
Along with rapid urbanization, the growth and persistence of slums is a global challenge. While remote sensing imagery is increasingly used for producing slum maps, only a few studies have analyzed their temporal dynamics. This study explores the potential of fully convolutional networks (FCNs) to analyze the temporal dynamics of small clusters of temporary slums using very high resolution (VHR) imagery in Bangalore, India. The study develops two approaches based on FCNs. The first approach uses a post-classification change detection, and the second trains FCNs to directly classify the dynamics of slums. For both approaches, the performances of 3 × 3 kernels and 5 × 5 kernels of the networks were compared. While classification results of individual years exhibit a relatively high F1-score (3 × 3 kernel) of 88.4% on average, the change accuracies are lower. The post-classification results obtained an F1-score of 53.8% and the change-detection networks obtained an F1-score of 53.7%. According to the trajectory error matrix (TEM), the post-classification results scored higher for the overall accuracy but lower for the accuracy difference of change trajectories than the change-detection networks. Although the two methods did not have significant differences in terms of accuracy, the change-detection network was less noisy. Within our study area, the areas of slums show a small overall decrease; the annual growth of slums (between 2012 and 2016) was 7173 m2, in contrast to an annual decline of 8390 m2. However, these numbers hid the spatial dynamics, which were much larger. Interestingly, areas where slums disappeared commonly changed into green areas, not into built-up areas. The proposed change-detection network provides a robust map of the locations of changes with lower confidence about the exact boundaries. This shows the potential of FCNs for detecting the dynamics of slums in VHR imagery. Full article
(This article belongs to the Special Issue Remote Sensing-Based Urban Planning Indicators)
Show Figures

Graphical abstract

Article
An Integrated Indicator Framework for the Assessment of Multifunctional Green Infrastructure—Exemplified in a European City
Remote Sens. 2019, 11(16), 1869; https://doi.org/10.3390/rs11161869 - 09 Aug 2019
Cited by 10 | Viewed by 2415
Abstract
The aim of this study is to provide an integrated indicator framework for the Assessment of Multifunctional Green Infrastructure (AMGI) to advance the evolution of the Green Infrastructure (GI) concept, and simultaneously deliver an approach do conduct a GI assessment using remote sensing [...] Read more.
The aim of this study is to provide an integrated indicator framework for the Assessment of Multifunctional Green Infrastructure (AMGI) to advance the evolution of the Green Infrastructure (GI) concept, and simultaneously deliver an approach do conduct a GI assessment using remote sensing datasets at multiple spatial and spectral scales. Based on this framework, we propose an explicit methodology for AMGI, while addressing the multi-dimensional pillars (ecology, socio-economy, socio-culture, and human health) for urban sustainability and the multifunctionality of GI. For the purpose of validation, we present the extensive process of employing our framework and methodology, and give an illustrative case exemplified in a European city, i.e., Leipzig, Germany. In this exemplification, we deployed three stages regarding how a single assessment can be conducted: from conceptual framework for priority setting, contextual assessment, to retrospective assessment. In this illustrative case study, we enclosed 18 indicators, as well as identified hot and cold spots of selected GI functions and their multifunctionality. A clear framework and methodology is crucial for the sustainable management of spatially oriented GI plans over time and for different stakeholder groups. Therefore, GI planners and policy makers may now refer to our integrative indicator framework and provided application methodology as common grounds for a better mutual understanding amongst scientists and stakeholders. This study contributes to discourses regarding the enhancement of the GI concept and is expected to provoke more discussion on the improvements of high-quality Remote Sensing (RS) data as well as the development of remote sensing-based methods at multiple spatial, temporal, and spectral scales to support GI plans. Full article
(This article belongs to the Special Issue Remote Sensing-Based Urban Planning Indicators)
Show Figures

Graphical abstract

Article
Identifying a Slums’ Degree of Deprivation from VHR Images Using Convolutional Neural Networks
Remote Sens. 2019, 11(11), 1282; https://doi.org/10.3390/rs11111282 - 29 May 2019
Cited by 19 | Viewed by 2574
Abstract
In the cities of the Global South, slum settlements are growing in size and number, but their locations and characteristics are often missing in official statistics and maps. Although several studies have focused on detecting slums from satellite images, only a few captured [...] Read more.
In the cities of the Global South, slum settlements are growing in size and number, but their locations and characteristics are often missing in official statistics and maps. Although several studies have focused on detecting slums from satellite images, only a few captured their variations. This study addresses this gap using an integrated approach that can identify a slums’ degree of deprivation in terms of socio-economic variability in Bangalore, India using image features derived from very high resolution (VHR) satellite images. To characterize deprivation, we use multiple correspondence analysis (MCA) and quantify deprivation with a data-driven index of multiple deprivation (DIMD). We take advantage of spatial features learned by a convolutional neural network (CNN) from VHR satellite images to predict the DIMD. To deal with a small training dataset of only 121 samples with known DIMD values, insufficient to train a deep CNN, we conduct a two-step transfer learning approach using 1461 delineated slum boundaries as follows. First, a CNN is trained using these samples to classify slums and formal areas. The trained network is then fine-tuned using the 121 samples to directly predict the DIMD. The best prediction is obtained by using an ensemble non-linear regression model, combining the results of the CNN and models based on hand-crafted and geographic information system (GIS) features, with R2 of 0.75. Our findings show that using the proposed two-step transfer learning approach, a deep CNN can be trained with a limited number of samples to predict the slums’ degree of deprivation. This demonstrates that the CNN-based approach can capture variations of deprivation in VHR images, providing a comprehensive understanding of the socio-economic situation of slums in Bangalore. Full article
(This article belongs to the Special Issue Remote Sensing-Based Urban Planning Indicators)
Show Figures

Graphical abstract

Article
3D Viewpoint Management and Navigation in Urban Planning: Application to the Exploratory Phase
Remote Sens. 2019, 11(3), 236; https://doi.org/10.3390/rs11030236 - 23 Jan 2019
Cited by 15 | Viewed by 2401
Abstract
3D geovisualization is essential in urban planning as it assists the analysis of geospatial data and decision making in the design and development of land use and built environment. However, we noted that 3D geospatial models are commonly visualized arbitrarily as current 3D [...] Read more.
3D geovisualization is essential in urban planning as it assists the analysis of geospatial data and decision making in the design and development of land use and built environment. However, we noted that 3D geospatial models are commonly visualized arbitrarily as current 3D viewers often lack of design instructions to assist end users. This is especially the case for the occlusion management in most 3D environments where the high density and diversity of 3D data to be displayed require efficient visualization techniques for extracting all the geoinformation. In this paper, we propose a theoretical and operational solution to manage occlusion by automatically computing best viewpoints. Based on user’s parameters, a viewpoint management algorithm initially calculates optimal camera settings for visualizing a set of 3D objects of interest through parallel projections. Precomputed points of view are then integrated into a flythrough creation algorithm for producing an automatic navigation within the 3D geospatial model. The algorithm’s usability is illustrated within the scope of a fictive exploratory phase for the public transport services access in the European quarter of Brussels. Eventually, the proposed algorithms may also assist additional urban planning phases in achieving their purposes. Full article
(This article belongs to the Special Issue Remote Sensing-Based Urban Planning Indicators)
Show Figures

Graphical abstract

Article
Road Information Extraction from High-Resolution Remote Sensing Images Based on Road Reconstruction
Remote Sens. 2019, 11(1), 79; https://doi.org/10.3390/rs11010079 - 04 Jan 2019
Cited by 18 | Viewed by 2432
Abstract
Traditional road extraction algorithms, which focus on improving the accuracy of road surfaces, cannot overcome the interference of shelter caused by vegetation, buildings, and shadows. In this paper, we extract the roads via road centerline extraction, road width extraction, broken centerline connection, and [...] Read more.
Traditional road extraction algorithms, which focus on improving the accuracy of road surfaces, cannot overcome the interference of shelter caused by vegetation, buildings, and shadows. In this paper, we extract the roads via road centerline extraction, road width extraction, broken centerline connection, and road reconstruction. We use a multiscale segmentation algorithm to segment the images, and feature extraction to get the initial road. The fast marching method (FMM) algorithm is employed to obtain the boundary distance field and the source distance field, and the branch backing-tracking method is used to acquire the initial centerline. Road width of each initial centerline is calculated by combining the boundary distance fields, before a tensor field is applied for connecting the broken centerline to gain the final centerline. The final centerline is matched with its road width when the final road is reconstructed. Three experimental results show that the proposed method improves the accuracy of the centerline and solves the problem of broken centerline, and that the method reconstructing the roads is excellent for maintain their integrity. Full article
(This article belongs to the Special Issue Remote Sensing-Based Urban Planning Indicators)
Show Figures

Figure 1

Other

Jump to: Editorial, Research

Letter
Night on South Korea: Unraveling the Relationship between Urban Development Patterns and DMSP-OLS Night-Time Lights
Remote Sens. 2019, 11(18), 2140; https://doi.org/10.3390/rs11182140 - 14 Sep 2019
Cited by 6 | Viewed by 3088
Abstract
Using artificial light data measured from satellites has the potential to change research methods in geography and urban planning. The Defense Meteorological Satellite Program Optical Linescan System (DMSP-OLS) night-time light datasets provided consistent and valuable data sources for investigating urbanization processes. This study [...] Read more.
Using artificial light data measured from satellites has the potential to change research methods in geography and urban planning. The Defense Meteorological Satellite Program Optical Linescan System (DMSP-OLS) night-time light datasets provided consistent and valuable data sources for investigating urbanization processes. This study intends to empirically investigate the relationship between night-time lights, population, and urban development patterns. A novel protocol was developed to integrate heterogeneous datasets into a standardized unit of analysis. Multivariate mixed-effects models were applied to detect correlations within and between provinces in South Korea. To capture physical variations of urban development, four landscape metrics were used and tested in the analyses. Diminishing returns of night-time lights to population were found in all models. In single landscape metric models, all coefficients of landscape metrics were positively related to night-time lights. In combination models, the aggregation index (AI) was no longer statistically significant. The protocol developed in this study provides an effective way to create analytical units for integrating heterogeneous forms of data. Creating standardized units of analyses will make it possible for researchers to compare their results with other studies. Landscape metrics used in this study for capturing the composition and configuration of urban development patterns will enrich the discussion in the future. Full article
(This article belongs to the Special Issue Remote Sensing-Based Urban Planning Indicators)
Show Figures

Graphical abstract

Back to TopTop