Hierarchical partition of urban land-use units by unsupervised graph learning from high-resolution satellite images
Urban land use information can be effectively extracted from high-resolution satellite images for many urban applications. A significant challenge remains the accurate partition of fine-grained land-use units from these images. This paper presents a novel method for deriving these units based on uns...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2024-12-01
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| Series: | International Journal of Digital Earth |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/17538947.2024.2432546 |
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| author | Mengmeng Li Xinyi Gai Kangkai Lou Alfred Stein |
| author_facet | Mengmeng Li Xinyi Gai Kangkai Lou Alfred Stein |
| author_sort | Mengmeng Li |
| collection | DOAJ |
| description | Urban land use information can be effectively extracted from high-resolution satellite images for many urban applications. A significant challenge remains the accurate partition of fine-grained land-use units from these images. This paper presents a novel method for deriving these units based on unsupervised graph learning techniques using high-resolution satellite images and open street boundaries. Our method constructs a graph to represent spatial relations between land cover objects as graph nodes within a street block. These nodes are characterized by spatial composition and structure features of their surrounding neighborhood. We then apply unsupervised graph learning to partition the graph into subgraphs, which represent communities spatially bounded by street boundaries and correspond to land use units. Next, a graph neural network is used to extract deep structural features for land use classification. Experiments were conducted using high-resolution satellite images from the cities of Fuzhou and Quanzhou, China. Results showed that our method surpassed traditional grid and street block techniques, improving land use classification accuracy by 24% and 9%, respectively. Furthermore, it achieved classification results comparable to those using reference land use units, with an overall accuracy of 0.87 versus 0.89. |
| format | Article |
| id | doaj-art-3b9e5df7a447446e90f7c23b0ce726b8 |
| 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-3b9e5df7a447446e90f7c23b0ce726b82025-08-20T02:07:23ZengTaylor & Francis GroupInternational Journal of Digital Earth1753-89471753-89552024-12-0117110.1080/17538947.2024.2432546Hierarchical partition of urban land-use units by unsupervised graph learning from high-resolution satellite imagesMengmeng Li0Xinyi Gai1Kangkai Lou2Alfred Stein3Key Laboratory of Spatial Data Mining & Information Sharing of Ministry of Education, Academy of Digital China (Fujian), Fuzhou University, Fuzhou, People's Republic of ChinaKey Laboratory of Spatial Data Mining & Information Sharing of Ministry of Education, Academy of Digital China (Fujian), Fuzhou University, Fuzhou, People's Republic of ChinaKey Laboratory of Spatial Data Mining & Information Sharing of Ministry of Education, Academy of Digital China (Fujian), Fuzhou University, Fuzhou, People's Republic of ChinaFaculty of Geo-Information Science and Earth Observation (ITC), University of Twente, Enschede, The NetherlandsUrban land use information can be effectively extracted from high-resolution satellite images for many urban applications. A significant challenge remains the accurate partition of fine-grained land-use units from these images. This paper presents a novel method for deriving these units based on unsupervised graph learning techniques using high-resolution satellite images and open street boundaries. Our method constructs a graph to represent spatial relations between land cover objects as graph nodes within a street block. These nodes are characterized by spatial composition and structure features of their surrounding neighborhood. We then apply unsupervised graph learning to partition the graph into subgraphs, which represent communities spatially bounded by street boundaries and correspond to land use units. Next, a graph neural network is used to extract deep structural features for land use classification. Experiments were conducted using high-resolution satellite images from the cities of Fuzhou and Quanzhou, China. Results showed that our method surpassed traditional grid and street block techniques, improving land use classification accuracy by 24% and 9%, respectively. Furthermore, it achieved classification results comparable to those using reference land use units, with an overall accuracy of 0.87 versus 0.89.https://www.tandfonline.com/doi/10.1080/17538947.2024.2432546Land-use unit partitionurban land use classificationgraph neural networksObject-Community-Block (OCB)high-resolution satellite images |
| spellingShingle | Mengmeng Li Xinyi Gai Kangkai Lou Alfred Stein Hierarchical partition of urban land-use units by unsupervised graph learning from high-resolution satellite images International Journal of Digital Earth Land-use unit partition urban land use classification graph neural networks Object-Community-Block (OCB) high-resolution satellite images |
| title | Hierarchical partition of urban land-use units by unsupervised graph learning from high-resolution satellite images |
| title_full | Hierarchical partition of urban land-use units by unsupervised graph learning from high-resolution satellite images |
| title_fullStr | Hierarchical partition of urban land-use units by unsupervised graph learning from high-resolution satellite images |
| title_full_unstemmed | Hierarchical partition of urban land-use units by unsupervised graph learning from high-resolution satellite images |
| title_short | Hierarchical partition of urban land-use units by unsupervised graph learning from high-resolution satellite images |
| title_sort | hierarchical partition of urban land use units by unsupervised graph learning from high resolution satellite images |
| topic | Land-use unit partition urban land use classification graph neural networks Object-Community-Block (OCB) high-resolution satellite images |
| url | https://www.tandfonline.com/doi/10.1080/17538947.2024.2432546 |
| work_keys_str_mv | AT mengmengli hierarchicalpartitionofurbanlanduseunitsbyunsupervisedgraphlearningfromhighresolutionsatelliteimages AT xinyigai hierarchicalpartitionofurbanlanduseunitsbyunsupervisedgraphlearningfromhighresolutionsatelliteimages AT kangkailou hierarchicalpartitionofurbanlanduseunitsbyunsupervisedgraphlearningfromhighresolutionsatelliteimages AT alfredstein hierarchicalpartitionofurbanlanduseunitsbyunsupervisedgraphlearningfromhighresolutionsatelliteimages |