A Review of the Structure of Street Networks

Findings, Journal Year: 2024, Volume and Issue: unknown

Published: Aug. 13, 2024

We review measures of street network structure proposed in the recent literature, establish their relevance to practice, and identify open challenges facing researchers. These measures’ empirical values vary substantially across world regions development eras, indicating networks’ geometric topological heterogeneity.

Language: Английский

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Turning points of the relationship between human activity and environmental quality in China

Sustainable Cities and Society, Journal Year: 2025, Volume and Issue: 119, P. 106123 - 106123

Published: Jan. 5, 2025

Language: Английский

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Formalising the urban pattern language: A morphological paradigm towards understanding the multi-scalar spatial structure of cities

Cities, Journal Year: 2025, Volume and Issue: 161, P. 105854 - 105854

Published: March 5, 2025

Language: Английский

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Global Variations of Urban Form: Characterization and Quantification through Intelligent Remote Sensing Image Analysis

Sustainable Cities and Society, Journal Year: 2025, Volume and Issue: unknown, P. 106424 - 106424

Published: May 1, 2025

Language: Английский

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Optimizing geometric and topological indices for sustainable mobility: a network design approach

Transportation, Journal Year: 2025, Volume and Issue: unknown

Published: April 17, 2025

Language: Английский

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Urban street network morphology classification through street-block based graph neural networks and multi-model fusion

International Journal of Digital Earth, Journal Year: 2025, Volume and Issue: 18(1)

Published: April 28, 2025

Language: Английский

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Contrastive Learning with Image Deformation and Refined NT-Xent Loss for Urban Morphology Discovery

ISPRS International Journal of Geo-Information, Journal Year: 2025, Volume and Issue: 14(5), P. 196 - 196

Published: May 8, 2025

The traditional paradigm for studying urban morphology involves the interpretation of Nolli maps, using methods such as morphometrics and visual neural networks. Previous studies on discovery have always been based raster analysis limited to central city area. Raster can lead fragmented forms, focusing only area ignores many representative forms in suburbs towns. In this study, a vast complex dataset was applied administrative community or village boundary, new image deformation pipeline proposed enhance morphological characteristics building groups. This allows networks focus extracting Additionally, research often uses unsupervised learning, which means that learning process is difficult control. Therefore, we refined NT-Xent loss so it integrate indicators. improvement network “recognize” similarity samples during optimization. By defining similarity, guide bring closer move them farther apart certain Three Chinese cities were used our testing. Representative types identified, particularly some located at fringe. data demonstrated effectiveness function, sociological illustrated unique functions these types.

Language: Английский

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Mapping Street Patterns with Network Science and Supervised Machine Learning

ISPRS International Journal of Geo-Information, Journal Year: 2024, Volume and Issue: 13(4), P. 114 - 114

Published: March 28, 2024

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 learning to classify networks into gridiron, organic, hybrid, and cul-de-sac with street-based local area (SLA) as unit of analysis. Utilising quantitative metrics GIS, analysed form through random forest method, which reveals predictive features enables deeper understanding spatial structures cities. The findings showed distinctive structures, such ring formations cores, indicating stages development socioeconomic narratives. It also analysis has major impact identification patterns. Concluding is critical tool suggests future studies should expand this include more cities elements. would enhance modelling growth inform sustainable, human-centric planning. implications are significant policymakers planners seeking harness data-driven insights

Language: Английский

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UrbanClassifier: A deep learning-based model for automated typology and temporal analysis of urban fabric across multiple spatial scales and viewpoints

Computers Environment and Urban Systems, Journal Year: 2024, Volume and Issue: 111, P. 102132 - 102132

Published: June 3, 2024

Language: Английский

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A detection method for road network interchanges with the MeshCNN based on Delaunay triangulation

International Journal of Digital Earth, Journal Year: 2024, Volume and Issue: 17(1)

Published: June 4, 2024

Due to their unstructured characteristics, mature convolutional neural network (CNN) models often have difficulty performing spatial analysis with vector data. Current studies used graph (GCN) address this problem. However, the definition of cognition factors involves uncertainties, making it challenging accurately and comprehensively define these factors. In paper, road interchange detection task is taken as an example introduce MeshCNN, a deep learning model based on triangular mesh data, aiming provide new solution for A edge classification first trained simple input features. Then, interchanges are detected results adaptive method. Experiments were conducted real-world data from four cities. The reveal that proposed method outperformed existing methods precision recall rate 89.36% 79.25% total datasets. Furthermore, our can also detect in other regions more easily than GCN

Language: Английский

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