Urban Planning Applications Research Articles

ARC-Net: An Efficient Network for Building Extraction From High-Resolution Aerial Images

Automatic building extraction based on high-resolution aerial images has important applications in urban planning and environmental management. In recent years advances and performance improvements have been achieved in building extraction through the use of deep learning methods. However, the design of existing models focuses attention to improve accuracy through an overflowing number of parameters and complex structure design, resulting in large computational costs during the learning phase and low inference speed. To address these issues, we propose a new, efficient end-to-end model, called ARC-Net. The model includes residual blocks with asymmetric convolution (RBAC) to reduce the computational cost and to shrink the model size. In addition, dilated convolutions and multi-scale pyramid pooling modules are utilized to enlarge the receptive field and to enhance accuracy. We verify the performance and efficiency of the proposed ARC-Net on the INRIA Aerial Image Labeling dataset and WHU building dataset. Compared to available deep learning models, the proposed ARC-Net demonstrates better segmentation performance with less computational costs. This indicates that the proposed ARC-Net is both effective and efficient in automatic building extraction from high-resolution aerial images.

Differential application of planning policy deepening the intracity divide: The case of greater Sydney, NSW, Australia

Urban planning policies in New South Wales (NSW), Australia are continuously being reformed, in order to make them more economic development friendly. These reforms are concerned with making development approvals easier and faster. The implementation of these reforms and their outcomes in Greater Sydney, NSW, vary according to the local socio-economic conditions. The affluent communities in Greater Sydney are very concerned about these reforms and actively resist their application in their areas. They are successful in avoiding the application of reformed urban planning policies. However, the lower socio-economic parts of Greater Sydney in the outer areas are not able to engage with these urban policy issues. The reformed urban policies are fully applied in the poorer areas, often resulting in excessive and poor-quality urban development. Past research on urban planning policy development, application and outcomes in Sydney has not investigated selective planning policy application and its differential outcomes. This paper analyses the selective application of some recent urban planning policy reforms as they relate to socio-economic division in Greater Sydney. The research argues that the selective application of urban planning policy in Greater Sydney is reinforcing socio-economic division there.

Daylight Provision Requirements According to EN 17037 as a Restriction for Sustainable Urban Planning of Residential Developments

The attempt at a more sustainable land use by increasing urban density may have a negative effect on the daylighting of residential buildings. In densely built areas, obstructions generated by the surrounding buildings can substantially reduce the available amount of daylight, causing poorly daylit spaces and a less healthy indoor environment with higher electricity consumption as a consequence of artificial lighting. European standard EN 17037, Daylight in Buildings, was established in 2018 to ensure appropriately daylit spaces. In this paper, a three-step methodology was developed to investigate the relationship between certain urban planning parameters and the daylighting of a typical room defined by specific (Slovenian) legislative restrictions about its geometry and minimum required window to floor area ratio, in order to establish the maximum densities of residential developments still fulfilling the minimum requirements for daylight provision defined by EN 17037. The results show that a relatively low urban density is required to fulfil the stipulations for minimum daylight provision for the deepest permissible room according to the Slovenian legislation. The impact of the development floor area ratio on the daylighting potential of buildings was identified as significant, followed by the site coverage, development layout, and building typology. Furthermore, the developed methodological approach clearly demonstrates a substantial potential for application in urban planning, with indoor daylight environmental conditions being linked to the planning of residential developments in the earliest stages of the project.

Real-time Traffic Assignment Using Engineered Customizable Contraction Hierarchies

Given an urban road network and a set of origin-destination pairs, the traffic assignment problem asks for the traffic flow on each road segment. Common solution algorithms require a large number of shortest-path computations. In this article, we significantly accelerate the computation of flow patterns, enabling interactive transportation and urban planning applications. We achieve this by building a traffic assignment procedure upon customizable contraction hierarchies (CCH), revisiting and carefully engineering CCH customization and queries, and adapting CCH to compute batched point-to-point shortest paths. Although motivated by the traffic assignment problem, our optimizations apply to CCH in general. In contrast to previous work, our evaluation uses real-world production data for all parts of the input. On a metropolitan area encompassing about 2.7 million inhabitants, we decrease the flow-pattern computation for a typical 1-hour morning peak (a quarter million trips) from 90.9 to 14.1 seconds on one core and 2.4 seconds on a 16-core machine. This represents a speedup of 37 over the state of the art and more than three orders of magnitude over the Dijkstra-based baseline.

Population Distributions of Age Groups and Their Influencing Factors Based on Mobile Phone Location Data: A Case Study of Beijing, China

The fine-grained population distributions of different age groups are crucial for urban planning applications. With the development of information and communication technology (ICT), detailed population data retrieved from various big data sources, especially on a fine scale, have been extensively used for urban planning. However, studies estimating the detailed population distributions of different age groups are still lacking. This study constructs a framework to generate fine-grained population data for different age groups and explores the influence of various factors on the distributions of different age groups. The population is divided into the following four age groups: (1) early adulthood people: 18 ≤ age ≤ 24, (2) young people: 25 ≤ age ≤ 39, (3) middle-aged people: 40 ≤ age ≤ 59, and (4) elderly people: 60 ≤ age. The results indicate that education and accommodation factors have a major influence on the distributions of early adulthood and elderly people, respectively. Business, restaurant, and accommodation factors are the main factors influencing the population distributions of young and middle-aged people. The accommodation factor plays a major controlling role at night, and its explanatory power gradually decreases during the day, while the explanatory powers of the business and restaurant factors increase and become leading factors during the day. Specifically, the hospital factor has a greater effect on the distribution of elderly people. The entertainment factor has very little explanatory power for the population distributions of the different age groups.

A 3D building reconstruction method for SAR images based on deep neural network

Owing to its momentous applications in urban planning, disaster monitoring, smart cities and other fields, 3D building reconstruction is an important topic in many research areas such as computer vision, photogrammetry, and remote sensing. The particularity and complexity of the microwave scattering mechanism bring great challenges to the 3D building reconstruction of SAR images, and the applicability and automation of existing methods need to be improved. This study constructs the overall framework of building detection in SAR images and 3D reconstruction based on deep learning and radar imaging mechanism. It puts forward a method of using a coupled equivalent complex valued convolutional neural network for building facade detection in SAR images, a method for RaySAR-based modeling simulation and point cloud generation for 3D model training, and a method for a 3D generation network for 3D building reconstruction from SAR images. Experiments using TerraSAR-X and GF-3 high resolution SAR images are carried out, producing good 3D reconstruction results. The proposed method is a useful attempt for 3D target reconstruction of SAR images and represents a new technical approach.

Seven-Component Model-Based Decomposition for PolSAR Data with Sophisticated Scattering Models

Due to incomprehensive and inaccurate scattering modeling, the state-of-the-art polarimetric synthetic aperture radar (PolSAR) model-based target decompositions are incapable of effectively depicting the scattering mechanism of obliquely oriented urban areas. In this paper, a seven-component model-based decomposition scheme is proposed by constructing several sophisticated scattering models. First, an eigenvalue-based obliquely-oriented dihedral scattering model is presented to reasonably distribute the co-polarization and cross-polarization scattering powers in obliquely oriented urban areas, thus accurately characterizing the urban scattering. Second, the ±45° oriented dipole and ±45° quarter-wave reflector scattering models are incorporated for the purpose of accounting for the real and imaginary components of the T 13 element in the coherency matrix so as to fully utilize polarimetric information. Finally, according to their mathematical forms, several strategies for model parameter solutions are designed, and the seven-component decomposition is fulfilled. Experimental results conducted on different PolSAR data demonstrate that the proposed method considerably improves the PolSAR scattering interpretation in a more physical manner compared to other existing model-based decomposition, which can be applied for urban area detection, classification, and other urban planning applications.

Building urban resilience with nature-based solutions: How can urban planning contribute?

Cities face increasing environmental, social and economic challenges that together threaten the resilience of urban areas and the residents who live and work there. These challenges include chronic stresses and acute shocks, amplified by climate change impacts. Nature-based solutions have emerged as a concept for integrating ecosystem-based approaches to address a range of societal challenges. Nature-based solutions directly address and contribute to increased urban resilience. However, implementing nature-based solutions is inherently complex, given the range of ecosystem services, their multi-functionality and the trade-offs between functions, and across temporal and spatial scales. Urban planning can play a substantial role to support the implementation of nature-based solutions and to manage trade-offs and conflicts, as well as how social equity dimensions are considered. This paper presents a framework that guides the application of urban planning to nature-based solutions’ implementation, by addressing key trade-offs across temporal, spatial, functional and social equity aspects. The framework highlights the key questions, and the supporting information required to address these questions, to underpin the inclusion of nature-based solutions for urban resilience. We find that while urban planning can contribute substantially, there are continuing gaps in how the inherently anthropocentric urban planning processes can give voice to non-human nature.

Aesthetic Consciousness of Literati Painting and its Application in Urban Planning

In order to find a way to combine traditional culture with modern living needs, taking “Chinese painting” as the breakthrough point, through the study of the development process and artistic characteristics of Chinese painting, four aspects of classical philosophy, natural landscape image, brush and ink composition artistic conception, and abstract aesthetic conception contained in Chinese painting are summed up. The results of the study provide enlightenment for contemporary residential landscape design, and summarize the methods of creating Chinese paintings in residential landscape design. Thus, a residential landscape model with the characteristics of “Chinese painting” is found out.

Building Stock and Building Typology of Kigali, Rwanda

This study uses very high-resolution Pléiades imagery for the densely built-up central part of the City of Kigali for the year 2015 in order to derive urban morphology data on building footprints, building archetypes and building heights. Aerial images of the study area from 2008–2009 were used in combination with the 2015 dataset to create a change monitoring dataset on a single building basis. A semi-automated approach was chosen which combined an object-based image analysis with an expert-based revision. The result is a geospatial dataset that detects 165,625 buildings for 2008–2009 and 211,458 for 2015. The dataset includes information on the type of changes between the two dates. Analysis of this geospatial dataset can be used for a range of research applications in economics and the social sciences, as well as a range of policy applications in urban planning and municipal finance administration.

An extended exploration and preferential return model for human mobility simulation at individual and collective levels

Human mobility models have prevalent applications in traffic management, urban planning, and disease prevention. Modeling human mobility combining both the statistical physical mechanics and geographical constrains is not fully investigated. In this research, we extend the exploration and preferential return (EPR) model by considering both spatial heterogeneity and distance decay. The extended model takes rank distance in the distance decay function, and is validated in four cities based on the population distribution and the trajectory data. Our model not only reproduces the statistics of human mobility at both the individual and collective levels with high accuracy, but also has a robust prediction at both high and low resolutions. The study demonstrates the potential of applications of the human mobility models aggregating the collective and individual levels factors, and sheds light on the trade-off between ‘simple’ mobility rules and ‘complex’ geographical environments.

健康影响评估及其在城市规划中的应用探讨 —— 以旧金山市东部邻里社区为例

健康影响评估及其在城市规划中的应用探讨 —— 以旧金山市东部邻里社区为例

Detection of ecological networks and connectivity with analyzing their effects on sustainable urban development

Rapid urbanization is a leading process for the global environmental problems such as climate change, massive loss of natural habitats, an increase of air, water, soil quality and social troubles. Within the scope of elimination of these effects, detecting, preserving and managing a strategically planned ecological network can provide ecological, economic, social and cultural benefits. Specially, connectivity of landscape patches in urban areas is an important factor for urban ecosystem cycle. Ecological studies under these circumstances are concentrated in urban areas and strategies are being developed to create green systems by establishing links between green areas. In this study, a method based on the graph theory has been proposed to create ecological links between important landscape patches in the Chennai City and the effects of the created system on the city has been discussed. Firstly, a comprehensive database is created for Chennai in the GIS. And then, important urban landscape patches and connectivity are detected with use of Conefor software that enhances the quality of landscape patches and ensures that landscape connectivity is sustainable. With this scope, we used integral index of connectivity (IIC) index and the probability of connectivity (PC) index that have been known to show an enhanced performance for urban habitat conservation planning and change monitoring applications. Ultimately, the resulting findings are mapped in the GIS environment, and the ecological, social and cultural impacts of the system are discussed based on international literature.

Spatio-temporal evolution of Ecologically-sustainable land use in China’s Loess Plateau and detection of its influencing factors

Ecological land (Eco-land) is a basic resource for human beings to survive, and eco-land use is a strategy, a way to manage the land resource. So, ecologically-sustainable land use is essential for human beings to survive. This paper investigates the spatiotemporal characteristics and mechanisms of urban-rural eco-land using a new and innovative integration way based on eco-land change data in China’s Loess Plateau (LP) prefecture level cities and explores factors of eco-land change. The spatial difference characteristic of eco-land among different level cities in the LP is that: small cities > big cities > middle cities. From 2009 to 2016, the eco-land in the LP from the perspective of urban-rural areas has changed significantly. Significant differences of urban-rural eco-land were identified among various urban growth types, and all the cities in the LP were further classified into four types based on eco-land change trend, with type A and B cities identified as the vital zone and major zone. Taking the eco-fragile region Loess Plateau (LP) as an example, our results demonstrated that the migrants to cities in LP could relieve ecological pressures and promote restoration of ecological vegetation. We have demonstrated that urbanization and the influence of government policy can be discerned through the quantification of the spatial-temporal change of eco-land and suggest that combining both urban and rural eco-land can support more effective land use decisions and provide theoretical basis for the practical application of urban planning, policy -making and sustainable development. What’s more, governments should strive to population mobility and restore vegetation to sustain this fragile ecological environment.

Road Extraction by Using Atrous Spatial Pyramid Pooling Integrated Encoder-Decoder Network and Structural Similarity Loss

The technology used for road extraction from remote sensing images plays an important role in urban planning, traffic management, navigation, and other geographic applications. Although deep learning methods have greatly enhanced the development of road extractions in recent years, this technology is still in its infancy. Because the characteristics of road targets are complex, the accuracy of road extractions is still limited. In addition, the ambiguous prediction of semantic segmentation methods also makes the road extraction result blurry. In this study, we improved the performance of the road extraction network by integrating atrous spatial pyramid pooling (ASPP) with an Encoder-Decoder network. The proposed approach takes advantage of ASPP’s ability to extract multiscale features and the Encoder-Decoder network’s ability to extract detailed features. Therefore, it can achieve accurate and detailed road extraction results. For the first time, we utilized the structural similarity (SSIM) as a loss function for road extraction. Therefore, the ambiguous predictions in the extraction results can be removed, and the image quality of the extracted roads can be improved. The experimental results using the Massachusetts Road dataset show that our method achieves an F1-score of 83.5% and an SSIM of 0.893. Compared with the normal U-net, our method improves the F1-score by 2.6% and the SSIM by 0.18. Therefore, it is demonstrated that the proposed approach can extract roads from remote sensing images more effectively and clearly than the other compared methods.

Extracting built-up areas from Sentinel-1 imagery using land-cover classification and texture analysis

ABSTRACT Urban areas are continuously expanding, against the background of accelerating urbanization, while their correct detection might be useful in a wide range of applications in urban planning and environmental studies. The paper proposes the development of a new method for the detection of the built-up areas based on SAR (Synthetic Aperture Radar) data, combining two images on ascendant and descendant orbits, while keeping both polarizations. In the pre-processing operation, we have obtained 4 backscattering bands, 4 primary texture bands, and 12 secondary texture bands which were subsequently used for classification purposes. The method combines a classification of the SAR images with a texture analysis (Iso-Tex) by using spectral signatures and a separation threshold. The results were compared to those achieved in a supervised classification for two cities in Central and Eastern Europe: Cluj-Napoca, in Romania and Wroclaw, in Poland. The potential of the new Iso-Tex method has been proven by the transition to a higher accuracy level compared to the supervised classification. The main advantage of the method is the separation of classes generating detection problems to both radar and optical systems (bare soil, excessively humid soil areas, and the upper part of the slopes).

Bikeability in Basel

“Bikeability” is becoming increasingly relevant in the field of transport- and urban planning. However, it is often unclear how bikeability is defined, let alone how it can be modeled. The goal of this project was to develop a quantitative method to model bikeability. A case study area in the city of Basel, Switzerland was selected for assessing the model. Here “bikeability” is understood as a measure of the ability and convenience in reaching important destinations by bike, based on the travel distance weighted by the perceived safety, -comfort, and -attractiveness of the streets and intersections along the routes. The underlying assumption was that cyclists try to minimize the distance traveled and maximize the perceived safety, -comfort, and -attractiveness of their route of choice. Unlike most of the previous bikeability assessments we reviewed, our method used existing route choice studies to identify attributes for quantifying cycling quality, which presumably results in a model that more accurately reflects real-life behavior. Many relevant attributes that have not been captured by previous models are included in this work, such as the high curbs of tram stops, tram tracks, and the turn direction at intersections. The method is suitable for several applications in urban planning, such as the identification of locations that need improvement and the comparison of planning measures. The current model covers conventional bikes used by commuting cyclists. However, the method could be used for E-bikes and non-commuting cyclists by applying the appropriate input values.

New parameter-free mobility model: Opportunity priority selection model

Predicting human mobility patterns has many practical applications in urban planning, traffic engineering, infectious disease epidemiology, emergency management and location-based services. Developing a universal model capable of accurately predicting the mobility fluxes between locations is a fundamental and challenging problem in regional economics and transportation science. Here, we propose a new parameter-free model named opportunity priority selection model as an alternative in human mobility prediction. The basic assumption of the model is that an individual will select destination locations that present higher opportunity benefits than the location opportunities of the origin and the intervening opportunities between the origin and destination. We use real mobility data collected from a number of cities and countries to demonstrate the predictive ability of this simple model. The results show that the new model offers universal predictions of intracity and intercity mobility patterns that are consistent with real observations, thus suggesting that the proposed model better captures the mechanism underlying human mobility than previous models.

Role of Emissivity in Lava Flow ‘Distance-to-Run’ Estimates from Satellite-Based Volcano Monitoring

Remote sensing is an established technological solution for bridging critical gaps in volcanic hazard assessment and risk mitigation. The enormous amount of remote sensing data available today at a range of temporal and spatial resolutions can aid emergency management in volcanic crises by detecting and measuring high-temperature thermal anomalies and providing lava flow propagation forecasts. In such thermal estimates, an important role is played by emissivity—the efficiency with which a surface radiates its thermal energy at various wavelengths. Emissivity has a close relationship with land surface temperatures and radiant fluxes, and it impacts directly on the prediction of lava flow behavior, as mass flux estimates depend on measured radiant fluxes. Since emissivity is seldom measured and mostly assumed, we aimed to fill this gap in knowledge by carrying out a multi-stage experiment, combining laboratory-based Fourier transform infrared (FTIR) analyses, remote sensing data, and numerical modeling. We tested the capacity for reproducing emissivity from spaceborne observations using ASTER Global Emissivity Database (GED) while assessing the spatial heterogeneity of emissivity. Our laboratory-satellite emissivity values were used to establish a realistic land surface temperature from a high-resolution spaceborne payload (ETM+) to obtain an instant temperature–radiant flux and eruption rate results for the 2001 Mount Etna (Italy) eruption. Forward-modeling tests conducted on the 2001 ‘aa’ lava flow by means of the MAGFLOW Cellular Automata code produced differences of up to ~600 m in the simulated lava flow ‘distance-to-run’ for a range of emissivity values. Given the density and proximity of urban settlements on and around Mount Etna, these results may have significant implications for civil protection and urban planning applications.

Overtourism Dystopias and Socialist Utopias: Towards an Urban Armature for Dubrovnik

ABSTRACTThe recent discourse on overtourism and anti-tourist attitudes has opened up the space to reimagine tourism development and planning. Employing an interdisciplinary approach we combined research by design methodology and rapid ethnography in order to problematise Dubrovnik's overtourism. The research turned for inspiration to the ex-Yugoslavian resorts and integrated planning. The paper advocates a praxical, socially informed and environmentally aware perspective and proposes interventions that offer the potential of practical applications in Dubrovnik's urban planning. Focusing on the need for connectivity and continuity the interventions address issues of segregation and marginalisation of local groups, such as students and seasonal workers. Inspired by the utopian ideals of socialist resorts, the research developed an urban armature that aims to connect the different parts of the old and modern city, reclaim tourist spaces for locals, and create open spaces in local areas.