Enhanced Automatic Identification of Urban Community Green Space Based on Semantic Segmentation

Jiangxi Chen, Siyu Shao, Yifei Zhu, Yu Wang, Fujie Rao, Xilei Dai and Dayi Lai
Additional contact information
Jiangxi Chen: Department of Architecture, School of Design, Shanghai Jiao Tong University, Shanghai 200240, China
Siyu Shao: Department of Architecture, School of Design, Shanghai Jiao Tong University, Shanghai 200240, China
Yifei Zhu: Department of Architecture, School of Design, Shanghai Jiao Tong University, Shanghai 200240, China
Yu Wang: Department of Architecture, School of Design, Shanghai Jiao Tong University, Shanghai 200240, China
Fujie Rao: Department of Architecture, School of Design, Shanghai Jiao Tong University, Shanghai 200240, China
Xilei Dai: Department of the Built Environment, National University of Singapore, 4 Architecture Drive, Singapore 117566, Singapore
Dayi Lai: Department of Architecture, School of Design, Shanghai Jiao Tong University, Shanghai 200240, China

Land, 2022, vol. 11, issue 6, 1-21

Abstract: At the neighborhood scale, recognizing urban community green space (UCGS) is important for residential living condition assessment and urban planning. However, current studies have embodied two key issues. Firstly, existing studies have focused on large geographic scales, mixing urban and rural areas, neglecting the accuracy of green space contours at fine geographic scales. Secondly, the green spaces covered by shadows often suffer misclassification. To address these issues, we created a neighborhood-scale urban community green space (UCGS) dataset and proposed a segmentation decoder for HRNet backbone with two auxiliary decoders. Our proposed model adds two additional branches to the low-resolution representations to improve their discriminative ability, thus enhancing the overall performance when the high- and low-resolution representations are fused. To evaluate the performance of the model, we tested it on a dataset that includes satellite images of Shanghai, China. The model outperformed the other nine models in UCGS extraction, with a precision of 83.01, recall of 85.69, IoU of 72.91, F1-score of 84.33, and OA of 89.31. Our model also improved the integrity of the identification of shaded green spaces over HRNetV2. The proposed method could offer a useful tool for efficient UCGS detection and mapping in urban planning.

Keywords: semantic segmentation; urban community green space; auxiliary learning; deep supervision; satellite images (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2073-445X/11/6/905/pdf (application/pdf)
https://www.mdpi.com/2073-445X/11/6/905/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jlands:v:11:y:2022:i:6:p:905-:d:838477

Access Statistics for this article

Land is currently edited by Ms. Carol Ma

More articles in Land from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().