Hierarchical Recognition for Urban Villages Fusing Multiview Feature Information
Urban village (UV) renovation is crucial for urban renewal, with effective UV recognition serving as a prerequisite. While existing studies on UV recognition predominantly rely on high-resolution remote sensing images (RSI), and few integrate street view images (SVI), which could cause confusion in...
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IEEE
2025-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
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| Online Access: | https://ieeexplore.ieee.org/document/10816389/ |
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| author | Zhenkang Wang Nan Xia Song Hua Jiale Liang Xiankai Ji Ziyu Wang Jiechen Wang |
| author_facet | Zhenkang Wang Nan Xia Song Hua Jiale Liang Xiankai Ji Ziyu Wang Jiechen Wang |
| author_sort | Zhenkang Wang |
| collection | DOAJ |
| description | Urban village (UV) renovation is crucial for urban renewal, with effective UV recognition serving as a prerequisite. While existing studies on UV recognition predominantly rely on high-resolution remote sensing images (RSI), and few integrate street view images (SVI), which could cause confusion in regions with similar planar features, such as old residential area and industrial parks. This article proposed a hierarchical framework for UV recognition which integrated multiview images. The spectral, textural, and structural features were extracted from Google RSI by machine-learning classifiers for each segmented block. The deep-learning method was applied to SVI to capture the architectural feature at each viewpoint. The rule-constrained fusion was conducted to combine the block-level and point-level UV recognition results. Taking a typical high-density megacity Nanjing as the study area, a high recognition overall accuracy (OA) and Kappa of 95.04% and 0.860 were achieved, identifying 172 UVs covering an area of 27.93 km<sup>2</sup> by 2020. The results demonstrated an “urban village ring” pattern in the city, with central urban areas showing a “multicenter and multicluster” spatial distribution, while suburban areas exhibited “large and concentrated” characteristics. Compared with results from single-view of RSI, the complementarity with SVI for multiview features increased the OA and Kappa by 3.34% and 0.079, which could effectively distinguish the old industrial parks. We believe that our proposed hierarchical framework is essential to the scientific and accurate UV recognition, which can guide the urban management and high-quality development. |
| format | Article |
| id | doaj-art-d1ee2b990df04ba3a8c86665af973eb0 |
| institution | Kabale University |
| issn | 1939-1404 2151-1535 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj-art-d1ee2b990df04ba3a8c86665af973eb02025-01-21T00:00:43ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352025-01-01183344335510.1109/JSTARS.2024.352266210816389Hierarchical Recognition for Urban Villages Fusing Multiview Feature InformationZhenkang Wang0https://orcid.org/0009-0006-3761-6265Nan Xia1https://orcid.org/0000-0002-2000-6018Song Hua2Jiale Liang3https://orcid.org/0000-0003-0639-0161Xiankai Ji4https://orcid.org/0009-0006-7977-3031Ziyu Wang5https://orcid.org/0009-0005-4463-4157Jiechen Wang6https://orcid.org/0000-0002-9022-8230Jiangsu Provincial Key Laboratory of Geographic Information Technology, Nanjing University, Nanjing, ChinaJiangsu Provincial Key Laboratory of Geographic Information Technology, Nanjing University, Nanjing, ChinaJiangsu Provincial Key Laboratory of Geographic Information Technology, Nanjing University, Nanjing, ChinaJiangsu Provincial Key Laboratory of Geographic Information Technology, Nanjing University, Nanjing, ChinaJiangsu Provincial Key Laboratory of Geographic Information Technology, Nanjing University, Nanjing, ChinaJiangsu Provincial Key Laboratory of Geographic Information Technology, Nanjing University, Nanjing, ChinaJiangsu Provincial Key Laboratory of Geographic Information Technology, Nanjing University, Nanjing, ChinaUrban village (UV) renovation is crucial for urban renewal, with effective UV recognition serving as a prerequisite. While existing studies on UV recognition predominantly rely on high-resolution remote sensing images (RSI), and few integrate street view images (SVI), which could cause confusion in regions with similar planar features, such as old residential area and industrial parks. This article proposed a hierarchical framework for UV recognition which integrated multiview images. The spectral, textural, and structural features were extracted from Google RSI by machine-learning classifiers for each segmented block. The deep-learning method was applied to SVI to capture the architectural feature at each viewpoint. The rule-constrained fusion was conducted to combine the block-level and point-level UV recognition results. Taking a typical high-density megacity Nanjing as the study area, a high recognition overall accuracy (OA) and Kappa of 95.04% and 0.860 were achieved, identifying 172 UVs covering an area of 27.93 km<sup>2</sup> by 2020. The results demonstrated an “urban village ring” pattern in the city, with central urban areas showing a “multicenter and multicluster” spatial distribution, while suburban areas exhibited “large and concentrated” characteristics. Compared with results from single-view of RSI, the complementarity with SVI for multiview features increased the OA and Kappa by 3.34% and 0.079, which could effectively distinguish the old industrial parks. We believe that our proposed hierarchical framework is essential to the scientific and accurate UV recognition, which can guide the urban management and high-quality development.https://ieeexplore.ieee.org/document/10816389/Bottom-up and top-downstreet viewsustainable urban renewalurban village (UV) |
| spellingShingle | Zhenkang Wang Nan Xia Song Hua Jiale Liang Xiankai Ji Ziyu Wang Jiechen Wang Hierarchical Recognition for Urban Villages Fusing Multiview Feature Information IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Bottom-up and top-down street view sustainable urban renewal urban village (UV) |
| title | Hierarchical Recognition for Urban Villages Fusing Multiview Feature Information |
| title_full | Hierarchical Recognition for Urban Villages Fusing Multiview Feature Information |
| title_fullStr | Hierarchical Recognition for Urban Villages Fusing Multiview Feature Information |
| title_full_unstemmed | Hierarchical Recognition for Urban Villages Fusing Multiview Feature Information |
| title_short | Hierarchical Recognition for Urban Villages Fusing Multiview Feature Information |
| title_sort | hierarchical recognition for urban villages fusing multiview feature information |
| topic | Bottom-up and top-down street view sustainable urban renewal urban village (UV) |
| url | https://ieeexplore.ieee.org/document/10816389/ |
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