Assessing Street Space Quality Using Street View Imagery and Function-Driven Method: The Case of Xiamen, China
Street space quality assessment refers to the extraction and appropriate evaluation of the space quality information of urban streets, which is usually employed to improve the quality of urban planning and management. Compared to traditional approaches relying on expert knowledge, the advances of bi...
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-04-01
|
| Series: | ISPRS International Journal of Geo-Information |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2220-9964/11/5/282 |
| _version_ | 1797499374342766592 |
|---|---|
| author | Moyang Wang Yijun He Huan Meng Ye Zhang Bao Zhu Joseph Mango Xiang Li |
| author_facet | Moyang Wang Yijun He Huan Meng Ye Zhang Bao Zhu Joseph Mango Xiang Li |
| author_sort | Moyang Wang |
| collection | DOAJ |
| description | Street space quality assessment refers to the extraction and appropriate evaluation of the space quality information of urban streets, which is usually employed to improve the quality of urban planning and management. Compared to traditional approaches relying on expert knowledge, the advances of big data collection and analysis technologies provide an alternative for assessing street space more precisely. With street view imagery (SVI), points of interest (POI) and comment data from social media, this study evaluates street space quality from the perspective of exploring and discussing the relationship among street vitality, service facilities and built environment. Firstly, a transfer-learning-based framework is employed for SVI semantic segmentation to quantify the street built environment. Then, we use POI data to identify different urban functions that streets serve, and comment data are utilized to investigate urban vitality composition and integrate it with different urban functions associated with streets. Finally, a function-driven street space quality assessment approach is established. To examine its applicability and performance, the proposed method is experimented with data from part area in Xiamen, China. The output is compared to results based on expert opinion using the correlation analysis method. Results show that the proposed assessment approach designed in this study is in accordance with the validation data, with the overall <i>R</i><sup>2</sup> value being greater than 0.6. In particular, the proposed method shows better performance in scenic land and mixed functional streets with <i>R</i><sup>2</sup> value being greater than 0.8. This method is expected to be an efficient tool for discovering problems and optimizing urban planning and management. |
| first_indexed | 2024-03-10T03:46:30Z |
| format | Article |
| id | doaj.art-884f06f2c0514daba13ac93b072b64b3 |
| institution | Directory Open Access Journal |
| issn | 2220-9964 |
| language | English |
| last_indexed | 2024-03-10T03:46:30Z |
| publishDate | 2022-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | ISPRS International Journal of Geo-Information |
| spelling | doaj.art-884f06f2c0514daba13ac93b072b64b32023-11-23T11:19:35ZengMDPI AGISPRS International Journal of Geo-Information2220-99642022-04-0111528210.3390/ijgi11050282Assessing Street Space Quality Using Street View Imagery and Function-Driven Method: The Case of Xiamen, ChinaMoyang Wang0Yijun He1Huan Meng2Ye Zhang3Bao Zhu4Joseph Mango5Xiang Li6Key Laboratory of Geographic Information Science (Ministry of Education), School of Geographic Sciences, East China Normal University, Shanghai 200241, ChinaKey Laboratory of Geographic Information Science (Ministry of Education), School of Geographic Sciences, East China Normal University, Shanghai 200241, ChinaKey Laboratory of Geographic Information Science (Ministry of Education), School of Geographic Sciences, East China Normal University, Shanghai 200241, ChinaKey Laboratory of Geographic Information Science (Ministry of Education), School of Geographic Sciences, East China Normal University, Shanghai 200241, ChinaKey Laboratory of Geographic Information Science (Ministry of Education), School of Geographic Sciences, East China Normal University, Shanghai 200241, ChinaKey Laboratory of Geographic Information Science (Ministry of Education), School of Geographic Sciences, East China Normal University, Shanghai 200241, ChinaKey Laboratory of Geographic Information Science (Ministry of Education), School of Geographic Sciences, East China Normal University, Shanghai 200241, ChinaStreet space quality assessment refers to the extraction and appropriate evaluation of the space quality information of urban streets, which is usually employed to improve the quality of urban planning and management. Compared to traditional approaches relying on expert knowledge, the advances of big data collection and analysis technologies provide an alternative for assessing street space more precisely. With street view imagery (SVI), points of interest (POI) and comment data from social media, this study evaluates street space quality from the perspective of exploring and discussing the relationship among street vitality, service facilities and built environment. Firstly, a transfer-learning-based framework is employed for SVI semantic segmentation to quantify the street built environment. Then, we use POI data to identify different urban functions that streets serve, and comment data are utilized to investigate urban vitality composition and integrate it with different urban functions associated with streets. Finally, a function-driven street space quality assessment approach is established. To examine its applicability and performance, the proposed method is experimented with data from part area in Xiamen, China. The output is compared to results based on expert opinion using the correlation analysis method. Results show that the proposed assessment approach designed in this study is in accordance with the validation data, with the overall <i>R</i><sup>2</sup> value being greater than 0.6. In particular, the proposed method shows better performance in scenic land and mixed functional streets with <i>R</i><sup>2</sup> value being greater than 0.8. This method is expected to be an efficient tool for discovering problems and optimizing urban planning and management.https://www.mdpi.com/2220-9964/11/5/282street view imageryurban functionstreet vitalitystreet space quality assessment |
| spellingShingle | Moyang Wang Yijun He Huan Meng Ye Zhang Bao Zhu Joseph Mango Xiang Li Assessing Street Space Quality Using Street View Imagery and Function-Driven Method: The Case of Xiamen, China ISPRS International Journal of Geo-Information street view imagery urban function street vitality street space quality assessment |
| title | Assessing Street Space Quality Using Street View Imagery and Function-Driven Method: The Case of Xiamen, China |
| title_full | Assessing Street Space Quality Using Street View Imagery and Function-Driven Method: The Case of Xiamen, China |
| title_fullStr | Assessing Street Space Quality Using Street View Imagery and Function-Driven Method: The Case of Xiamen, China |
| title_full_unstemmed | Assessing Street Space Quality Using Street View Imagery and Function-Driven Method: The Case of Xiamen, China |
| title_short | Assessing Street Space Quality Using Street View Imagery and Function-Driven Method: The Case of Xiamen, China |
| title_sort | assessing street space quality using street view imagery and function driven method the case of xiamen china |
| topic | street view imagery urban function street vitality street space quality assessment |
| url | https://www.mdpi.com/2220-9964/11/5/282 |
| work_keys_str_mv | AT moyangwang assessingstreetspacequalityusingstreetviewimageryandfunctiondrivenmethodthecaseofxiamenchina AT yijunhe assessingstreetspacequalityusingstreetviewimageryandfunctiondrivenmethodthecaseofxiamenchina AT huanmeng assessingstreetspacequalityusingstreetviewimageryandfunctiondrivenmethodthecaseofxiamenchina AT yezhang assessingstreetspacequalityusingstreetviewimageryandfunctiondrivenmethodthecaseofxiamenchina AT baozhu assessingstreetspacequalityusingstreetviewimageryandfunctiondrivenmethodthecaseofxiamenchina AT josephmango assessingstreetspacequalityusingstreetviewimageryandfunctiondrivenmethodthecaseofxiamenchina AT xiangli assessingstreetspacequalityusingstreetviewimageryandfunctiondrivenmethodthecaseofxiamenchina |