SurfaceAI: Automated creation of cohesive road surface quality datasets based on open street-level imagery DOI Creative Commons
Alexandra Kapp,

E. Hoffmann,

Esther Weigmann

et al.

Published: Oct. 29, 2024

This paper introduces SurfaceAI, a pipeline designed to generate comprehensive georeferenced datasets on road surface type and quality from openly available street-level imagery. The motivation stems the significant impact of unevenness safety comfort traffic participants, especially vulnerable users, emphasizing need for detailed data in infrastructure modeling analysis. SurfaceAI addresses this gap by leveraging crowdsourced Mapillary train models that predict surfaces visible images, which are then aggregated provide cohesive information entire segment conditions.

Language: Английский

Coverage and bias of street view imagery in mapping the urban environment DOI
Zicheng Fan, Chen‐Chieh Feng, Filip Biljecki

et al.

Computers Environment and Urban Systems, Journal Year: 2025, Volume and Issue: 117, P. 102253 - 102253

Published: Jan. 23, 2025

Language: Английский

Citations

0
ZenSVI: An open-source software for the integrated acquisition, processing and analysis of street view imagery towards scalable urban science DOI
Koichi Ito, Yihan Zhu, Mahmoud Abdelrahman

et al.

Computers Environment and Urban Systems, Journal Year: 2025, Volume and Issue: 119, P. 102283 - 102283

Published: March 20, 2025

Language: Английский

Citations

0
Exploring the drivers of Walkability: Implications for enhancing perception and policy to livable cities DOI Creative Commons
Yitayal Addis Alemayehu, Bewketu Mamaru Mengiste, Gebrie Tsegaye Mersha

et al.

City and Environment Interactions, Journal Year: 2025, Volume and Issue: unknown, P. 100197 - 100197

Published: March 1, 2025

Language: Английский

Citations

0
Walkability at Street Level: An Indicator-Based Assessment Model DOI Open Access
Petra Stutz, Dana Kaziyeva, Christoph Traun

et al.

Sustainability, Journal Year: 2025, Volume and Issue: 17(8), P. 3634 - 3634

Published: April 17, 2025

Walking is recognised as a healthy and sustainable mode of transport. Providing adequate infrastructure pivotal for the promotion walking and, subsequently, achieving benefits derived from its numerous positive effects. However, efficiently measuring walkability at street level remains challenging. In this paper, we present an indicator-based assessment model that can be used with open spatial data to evaluate segment-based walkability. The incorporates eleven indicators describing segments their close surroundings are relevant pedestrians, such presence type pedestrian infrastructure, road category, noise levels, exposure green blue space. A weighted average calculation results in index values each segment within network graph. model’s generic approach ability ensure reproducibility, adaptability, scalability. feasibility was shown using case study Salzburg, Austria. validity evaluated through large-scale involving 660 full responses online survey. Participants provided ratings on randomly selected which were compared calculated index, revealing strong correlation (Spearman’s rank = 0.82).

Language: Английский

Citations

0
A panorama-based technique to estimate sky view factor and solar irradiance considering transmittance of tree canopies DOI Creative Commons
Kunihiko Fujiwara, Koichi Ito, Marcel Ignatius

et al.

Building and Environment, Journal Year: 2024, Volume and Issue: 266, P. 112071 - 112071

Published: Sept. 13, 2024

Language: Английский

Citations

3
Integrating Streetscape Images, Machine Learning, and Space Syntax to Enhance Walkability: A Case Study of Seongbuk District, Seoul DOI Creative Commons

Zhongshan Huang,

Wang Bin, Shixian Luo

et al.

Land, Journal Year: 2024, Volume and Issue: 13(10), P. 1591 - 1591

Published: Sept. 30, 2024

As urbanization rapidly progresses, streets have transitioned from mere transportation corridors to crucial spaces for daily life and social interaction. While past research has examined the impact of physical street characteristics on walkability, there is still a lack large-scale quantitative assessments. This study systematically evaluates walkability in Seongbuk District, Seoul, through integration streetscape images, machine learning, space syntax. The were extracted analyzed conjunction with syntax assess accessibility, leading combined analysis accessibility. results reveal that central western regions District outperform eastern overall performance. Additionally, identifies four distinct types based their spatial distribution: high accessibility–high score, accessibility–low low score. findings not only provide scientific basis development but also offer valuable insights assessing enhancing cities globally.

Language: Английский

Citations

3
SurfaceAI: Automated creation of cohesive road surface quality datasets based on open street-level imagery DOI Creative Commons
Alexandra Kapp,

E. Hoffmann,

Esther Weigmann

et al.

Published: Oct. 29, 2024

This paper introduces SurfaceAI, a pipeline designed to generate comprehensive georeferenced datasets on road surface type and quality from openly available street-level imagery. The motivation stems the significant impact of unevenness safety comfort traffic participants, especially vulnerable users, emphasizing need for detailed data in infrastructure modeling analysis. SurfaceAI addresses this gap by leveraging crowdsourced Mapillary train models that predict surfaces visible images, which are then aggregated provide cohesive information entire segment conditions.

Language: Английский

Citations

0