Large-Scale Spatial–Temporal Identification of Urban Vacant Land and Informal Green Spaces Using Semantic Segmentation

• Published in: Remote sensing (Basel, Switzerland) Switzerland), 2024-01, Vol.16 (2), p.216
• Main Authors: Hu, Xinlei, Zhuang, Shuo
• Format: Article
• Language: English
• Subjects: Artificial intelligence; Biodiversity; Cities; Comparative analysis; Datasets; Deep learning; Environmental aspects; Environmental impact; Green infrastructure; Identification; Identification and classification; Identification methods; Image segmentation; informal green spaces; Land use; Machine learning; Multilayer perceptrons; Performance evaluation; Remote sensing; Semantic segmentation; Semantics; Spatial analysis; spatial–temporal changes; Urban areas; Urban land use; urban vacant land; Vegetation
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs16020216

The large-scale identification of urban vacant land (UVL) and informal green spaces (IGSs) using conventional identification methods is challenged by the high cost of time and resources as well as inconsistent outcomes. Moreover, the spatial–temporal changes of UVL and IGSs have received limited academic attention. We introduce a methodological framework for the large-scale automatic identification of UVL and IGSs in Hangzhou, China, based on semantic segmentation. We construct and release a large-scale dataset for UVL identification, containing five different UVL categories, and one of them is IGS, with 3096 patches for training and 128 patches for evaluation. We then train five different semantic segmentation networks using the dataset and utilise Segformer to predict UVL and IGSs within the whole urban area of Hangzhou. The presented segmentation model has a hierarchically structured Transformer encoder and a multilayer perceptron decoder, which incorporates local and global information to obtain effective feature representations. Extensive experiments have been conducted to evaluate the segmentation performance of the applied Segformer. Experimental results show the good identification performance of Segformer for UVL and IGSs. Results also verify that the proposed identification framework can be effectively used to analyse the spatial–temporal changes of UVL and IGSs in Hangzhou.