IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v16y2024i2p833-d1321598.html
   My bibliography  Save this article

Semantic and Instance Segmentation in Coastal Urban Spatial Perception: A Multi-Task Learning Framework with an Attention Mechanism

Author

Listed:
  • Hanwen Zhang

    (Department of Marine Design Convergence Engineering, Pukyong National University, 45, Yongso-ro, Nam-Gu, Busan 48513, Republic of Korea)

  • Hongyan Liu

    (Department of Marine Design Convergence Engineering, Pukyong National University, 45, Yongso-ro, Nam-Gu, Busan 48513, Republic of Korea)

  • Chulsoo Kim

    (Department of Marine Design Convergence Engineering, Pukyong National University, 45, Yongso-ro, Nam-Gu, Busan 48513, Republic of Korea)

Abstract

With the continuous acceleration of urbanization, urban planning and design require more in-depth research and development. Street view images can express rich urban features and guide residents’ emotions toward a city, thereby providing the most intuitive reflection of their perception of the city’s spatial quality. However, current researchers mainly conduct research on urban spatial quality through subjective experiential judgment, which includes problems such as a high cost and a low judgment accuracy. In response to these problems, this study proposes a multi-task learning urban spatial attribute perception model that integrates an attention mechanism. Via this model, the existing attributes of urban street scenes are analyzed. Then, the model is improved by introducing semantic segmentation and instance segmentation to identify and match the qualities of the urban space. The experimental results show that the multi-task learning urban spatial attribute perception model with an integrated attention mechanism has prediction accuracies of 79.54%, 78.62%, 79.68%, 77.42%, 78.45%, and 76.98% for the urban spatial attributes of beauty, boredom, depression, liveliness, safety, and richness, respectively. The accuracy of the multi-task learning urban spatial scene feature image segmentation model with an integrated attention mechanism is 95.4, 94.8, 96.2, 92.1, and 96.7 for roads, walls, sky, vehicles, and buildings, respectively. The multi-task learning urban spatial scene feature image segmentation model with an integrated attention mechanism has a higher recognition accuracy for urban spatial buildings than other models. These research results indicate the model’s effectiveness in matching urban spatial quality with public perception.

Suggested Citation

  • Hanwen Zhang & Hongyan Liu & Chulsoo Kim, 2024. "Semantic and Instance Segmentation in Coastal Urban Spatial Perception: A Multi-Task Learning Framework with an Attention Mechanism," Sustainability, MDPI, vol. 16(2), pages 1-14, January.
  • Handle: RePEc:gam:jsusta:v:16:y:2024:i:2:p:833-:d:1321598
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/16/2/833/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/16/2/833/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Tao Shi & Yurong Qiao & Qian Zhou, 2021. "Spatiotemporal evolution and spatial relevance of urban resilience: Evidence from cities of China," Growth and Change, Wiley Blackwell, vol. 52(4), pages 2364-2390, December.
    2. Hsien-Hsin Cheng & Yi-Ya Hsu, 2022. "Integrating spatial multi-criteria evaluation into the potential analysis of culture-led urban development – A case study of Tainan," Environment and Planning B, , vol. 49(1), pages 335-351, January.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yizhen Zhang & Zhen Deng & Agus Supriyadi & Rui Song & Tao Wang, 2022. "Spatiotemporal spread characteristics and influencing factors of COVID‐19 cases: Based on big data of population migration in China," Growth and Change, Wiley Blackwell, vol. 53(4), pages 1694-1715, December.
    2. Liangang Li & Pingyu Zhang & Chengxin Wang, 2022. "What Affects the Economic Resilience of China’s Yellow River Basin Amid Economic Crisis—From the Perspective of Spatial Heterogeneity," IJERPH, MDPI, vol. 19(15), pages 1-20, July.
    3. Tingting Yang & Lin Wang, 2024. "Did Urban Resilience Improve during 2005–2021? Evidence from 31 Chinese Provinces," Land, MDPI, vol. 13(3), pages 1-22, March.
    4. Chenchen Shi & Xiaoping Zhu & Haowei Wu & Zhihui Li, 2022. "Assessment of Urban Ecological Resilience and Its Influencing Factors: A Case Study of the Beijing-Tianjin-Hebei Urban Agglomeration of China," Land, MDPI, vol. 11(6), pages 1-14, June.
    5. Yu Chen & Xuyang Su & Qian Zhou, 2021. "Study on the Spatiotemporal Evolution and Influencing Factors of Urban Resilience in the Yellow River Basin," IJERPH, MDPI, vol. 18(19), pages 1-20, September.
    6. Huiping Wang & Qi Ge, 2023. "Spatial association network of economic resilience and its influencing factors: evidence from 31 Chinese provinces," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-14, December.
    7. Muxi Yang & Guofang Zhai, 2024. "Measurement and Influencing Factors of Economic Resilience over a Long Duration of COVID-19: A Case Study of the Yangtze River Delta, China," Land, MDPI, vol. 13(2), pages 1-22, February.
    8. Huali Pan & Yuxin Yang & Wei Zhang & Mingzhi Xu, 2024. "Research on Coupling Coordination of China’s Urban Resilience and Tourism Economy—Taking Yangtze River Delta City Cluster as an Example," Sustainability, MDPI, vol. 16(3), pages 1-27, February.
    9. Guiyuan Li & Guo Cheng & Zhenying Wu & Xiaoxiao Liu, 2022. "Coupling Coordination Research on Disaster-Adapted Resilience of Modern Infrastructure System in the Middle and Lower Section of the Three Gorges Reservoir Area," Sustainability, MDPI, vol. 14(21), pages 1-24, November.
    10. Wang, Ke-Liang & Jiang, Wei & Miao, Zhuang, 2023. "Impact of high-speed railway on urban resilience in China: Does urban innovation matter?," Socio-Economic Planning Sciences, Elsevier, vol. 87(PB).
    11. Ke Liu & Shiwen Yang & Qian Zhou & Yurong Qiao, 2021. "Spatiotemporal Evolution and Spatial Network Analysis of the Urban Ecological Carrying Capacity in the Yellow River Basin," IJERPH, MDPI, vol. 19(1), pages 1-25, December.
    12. Chenchen Shi & Xiaoping Zhu & Haowei Wu & Zhihui Li, 2022. "Urbanization Impact on Regional Sustainable Development: Through the Lens of Urban-Rural Resilience," IJERPH, MDPI, vol. 19(22), pages 1-17, November.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:16:y:2024:i:2:p:833-:d:1321598. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.