Assessing semantic consistency of pavement markings and signs using street view images – a case study on lane-turning information
Road asset management (RAM) is crucial in road construction and maintenance. Previous efforts have focused on the digitization of the physical state of road facilities, such as location and condition. However, the semantic information conveyed by these facilities, such as instructions, controls, and...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2024-12-01
|
| Series: | International Journal of Digital Earth |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/17538947.2024.2436491 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850137440100024320 |
|---|---|
| author | Jinlin Shi Xiao Li Bingxian Lin Liangchen Zhou Guonian Lv |
| author_facet | Jinlin Shi Xiao Li Bingxian Lin Liangchen Zhou Guonian Lv |
| author_sort | Jinlin Shi |
| collection | DOAJ |
| description | Road asset management (RAM) is crucial in road construction and maintenance. Previous efforts have focused on the digitization of the physical state of road facilities, such as location and condition. However, the semantic information conveyed by these facilities, such as instructions, controls, and warnings, and the consistency of semantic information across multiple facilities has been neglected. Inconsistent semantic information can confuse road users, disrupt traffic, and endanger lives. To address this critical problem, this study proposes the concept of ‘semantic space’ for road facilities and presents a comprehensive framework that combines street view images with deep learning techniques to detect, localize, and analyze semantic consistency with this space, specifically focusing on lane-turning information. To validate the effectiveness of our framework, we conducted experiments on 81 km of urban roads in Nanjing, Jiangsu, China. The experimental results show that our method has an overall precision of 77.6% and an overall recall of 94.2% for detecting defined semantic inconsistency errors. While this study focuses on lane-turning information, the proposed framework for semantic space detection and assessment shows promise in analyzing inconsistencies in other road semantic information conveyed by diverse and discrete road facilities, contributing to an enhanced RAM. |
| format | Article |
| id | doaj-art-e8e49555af31479e989fac9161829c63 |
| institution | OA Journals |
| issn | 1753-8947 1753-8955 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | International Journal of Digital Earth |
| spelling | doaj-art-e8e49555af31479e989fac9161829c632025-08-20T02:30:50ZengTaylor & Francis GroupInternational Journal of Digital Earth1753-89471753-89552024-12-0117110.1080/17538947.2024.2436491Assessing semantic consistency of pavement markings and signs using street view images – a case study on lane-turning informationJinlin Shi0Xiao Li1Bingxian Lin2Liangchen Zhou3Guonian Lv4School of Geography Science, Nanjing Normal University, Nanjing, People’s Republic of ChinaTransport Studies Unit, University of Oxford, Oxford, UKSchool of Geography Science, Nanjing Normal University, Nanjing, People’s Republic of ChinaSchool of Geography Science, Nanjing Normal University, Nanjing, People’s Republic of ChinaSchool of Geography Science, Nanjing Normal University, Nanjing, People’s Republic of ChinaRoad asset management (RAM) is crucial in road construction and maintenance. Previous efforts have focused on the digitization of the physical state of road facilities, such as location and condition. However, the semantic information conveyed by these facilities, such as instructions, controls, and warnings, and the consistency of semantic information across multiple facilities has been neglected. Inconsistent semantic information can confuse road users, disrupt traffic, and endanger lives. To address this critical problem, this study proposes the concept of ‘semantic space’ for road facilities and presents a comprehensive framework that combines street view images with deep learning techniques to detect, localize, and analyze semantic consistency with this space, specifically focusing on lane-turning information. To validate the effectiveness of our framework, we conducted experiments on 81 km of urban roads in Nanjing, Jiangsu, China. The experimental results show that our method has an overall precision of 77.6% and an overall recall of 94.2% for detecting defined semantic inconsistency errors. While this study focuses on lane-turning information, the proposed framework for semantic space detection and assessment shows promise in analyzing inconsistencies in other road semantic information conveyed by diverse and discrete road facilities, contributing to an enhanced RAM.https://www.tandfonline.com/doi/10.1080/17538947.2024.2436491Road asset managementsemantic informationconsistencystreet view images |
| spellingShingle | Jinlin Shi Xiao Li Bingxian Lin Liangchen Zhou Guonian Lv Assessing semantic consistency of pavement markings and signs using street view images – a case study on lane-turning information International Journal of Digital Earth Road asset management semantic information consistency street view images |
| title | Assessing semantic consistency of pavement markings and signs using street view images – a case study on lane-turning information |
| title_full | Assessing semantic consistency of pavement markings and signs using street view images – a case study on lane-turning information |
| title_fullStr | Assessing semantic consistency of pavement markings and signs using street view images – a case study on lane-turning information |
| title_full_unstemmed | Assessing semantic consistency of pavement markings and signs using street view images – a case study on lane-turning information |
| title_short | Assessing semantic consistency of pavement markings and signs using street view images – a case study on lane-turning information |
| title_sort | assessing semantic consistency of pavement markings and signs using street view images a case study on lane turning information |
| topic | Road asset management semantic information consistency street view images |
| url | https://www.tandfonline.com/doi/10.1080/17538947.2024.2436491 |
| work_keys_str_mv | AT jinlinshi assessingsemanticconsistencyofpavementmarkingsandsignsusingstreetviewimagesacasestudyonlaneturninginformation AT xiaoli assessingsemanticconsistencyofpavementmarkingsandsignsusingstreetviewimagesacasestudyonlaneturninginformation AT bingxianlin assessingsemanticconsistencyofpavementmarkingsandsignsusingstreetviewimagesacasestudyonlaneturninginformation AT liangchenzhou assessingsemanticconsistencyofpavementmarkingsandsignsusingstreetviewimagesacasestudyonlaneturninginformation AT guonianlv assessingsemanticconsistencyofpavementmarkingsandsignsusingstreetviewimagesacasestudyonlaneturninginformation |