InstantCITY: Synthesising morphologically accurate geospatial data for urban form analysis, transfer, and quality control

Wu, Abraham Noah; Biljecki, Filip

doi:10.1016/j.isprsjprs.2022.11.005

Cited by 14 publications

(5 citation statements)

References 62 publications

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“…However, the common premise for these street patterns to be utilised in modern morphological studies is that they can be digitalised for quantitative analysis like other urban elements. They can either be stored as a networked structure, facilitating urban morphology analysis with their network metrics like connectivity [22,23], or stored as a raster file, providing detailed information for computer vision-based analysis [24,25].…”

Section: Literature Review 21 Street Patterns In Urban Morphologymentioning

confidence: 99%

“…Finally, we have the street-based local area (SLA), which divides the unit of analysis based on the street's network structure. The SLA is a relatively new unit of analysis in urban studies that was first introduced by Stephan Law [25]. After that, it was further developed to incorporate other urban elements for a more refined capture of the local area [10].…”

Section: Machine Learning Framework For Street Morphologymentioning

confidence: 99%

See 1 more Smart Citation

Mapping Street Patterns with Network Science and Supervised Machine Learning

Wu,

Wang,

Wang

et al. 2024

IJGI

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This study introduces a machine learning-based framework for mapping street patterns in urban morphology, offering an objective, scalable approach that transcends traditional methodologies. Focusing on six diverse cities, the research employed supervised machine learning to classify street networks into gridiron, organic, hybrid, and cul-de-sac patterns with the street-based local area (SLA) as the unit of analysis. Utilising quantitative street metrics and GIS, the study analysed the urban form through the random forest method, which reveals the predictive features of urban patterns and enables a deeper understanding of the spatial structures of cities. The findings showed distinctive spatial structures, such as ring formations and urban cores, indicating stages of urban development and socioeconomic narratives. It also showed that the unit of analysis has a major impact on the identification and study of street patterns. Concluding that machine learning is a critical tool in urban morphology, the research suggests that future studies should expand this framework to include more cities and urban elements. This would enhance the predictive modelling of urban growth and inform sustainable, human-centric urban planning. The implications of this study are significant for policymakers and urban planners seeking to harness data-driven insights for the development of cities.

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Section: Literature Review 21 Street Patterns In Urban Morphologymentioning

confidence: 99%

Section: Machine Learning Framework For Street Morphologymentioning

confidence: 99%

Mapping Street Patterns with Network Science and Supervised Machine Learning

Wu,

Wang,

Wang

et al. 2024

IJGI

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“…Additionally, a heuristic technique using Matlab is evidenced [14]. The synthesis of geospatial data morphologically intended for the transfer and quality control of urban form is presented as a contribution of georeferencing for development prediction and urban planning [15]. A detailed schedule-based transit network is proposed to measure accessibility to hospitals based on transit in a growing city [16].…”

Section: Related Workmentioning

confidence: 99%

“…Objectives Electricity Drinking Water Data Gas Transport Other Garcia, 2023 [6] Power Network Planning ✓ ✓ Kim, 2023 [16] Healthcare accessibility ✓ ✓ ✓ Wu, 2023 [15] Urban Development ✓ ✓ ✓ Gaugl, 2023 [14] Power Network Planning ✓ ✓ Kersapati, 2023 [17] Urban Management ✓ ✓ ✓ Kim, 2022 [25] Small unmanned aircraft ✓ ✓ Song, 2022 [23] Remote sensing -Urban planning ✓ ✓ Hacar, 2022 [22] Urban planning ✓ ✓ Hellekes, 2022 [20] Urban Planning ✓ ✓ ✓ Zourlidou, 2022 [19] Traffic Engineering ✓ ✓ ✓ Milleville, 2022 [24] Gerreferencing…”

Section: Applications Author Yearmentioning

confidence: 99%

Georeferencing for Planning Underground Networks

Inga Ortega

2024

Digital Technology for Smart Grid Innovative Algorithmic Solutions for Engineering Problems

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“…The world's knowledge has improved due to the rapid rise of the geospatial data industry over the past few decades (Akylbekov et al, 2022). Geospatial data-derived insights and technologies address many complicated issues, from improving fuel economy by tracking logistical networks to creating cities prioritising sustainability (Wu and Biljecki, 2023).…”

Section: Geospatial Data Analysis Techniques For Smart City Planningmentioning

confidence: 99%

Geospatial Techniques for Preparing the Requirements of 3D Modeling for Smart City Planning- Review paper

Sadiqe,

Jasim,

Al-Bakri

2023

IQJAP

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Geospatial technique, 3D modelling, Smart city.Developing smart city planning requires integrating various techniques, including geospatial techniques, building information models (BIM), information and communication technology (ICT), and artificial intelligence, for instance, threedimensional (3D) building models, in enabling smart city applications. This study aims to comprehensively analyze the role and significance of geospatial techniques in smart city planning and implementation. The literature review encompasses (74) studies from diverse databases, examining relevant solutions and prototypes related to smart city planning. The focus highlights the requirements and preparation of geospatial techniques to support the transition to a smart city. The paper explores various aspects, such as the advantages and challenges of geospatial techniques, data collection and analysis methodologies, and case studies showcasing successful implementations of smart city initiatives. The research concludes that geospatial techniques are instrumental in driving the development of smart cities. By analyzing and synthesizing the outcomes of the reviewed articles, this study establishes the essential contribution of geospatial techniques in successfully realizing the vision of smart cities.

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InstantCITY: Synthesising morphologically accurate geospatial data for urban form analysis, transfer, and quality control

Cited by 14 publications

References 62 publications

Mapping Street Patterns with Network Science and Supervised Machine Learning

Mapping Street Patterns with Network Science and Supervised Machine Learning

Georeferencing for Planning Underground Networks

Geospatial Techniques for Preparing the Requirements of 3D Modeling for Smart City Planning- Review paper

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