Road Damage Detection From Post-Disaster High-Resolution Remote Sensing Images Based on TLD Framework
Road is one of important traffic lifelines that could be damaged after disaster by landslide rubble, buildings debris, and collapsed branches of trees. Therefore, road damage detection and assessment using post-Disaster High-Resolution Remote Sensing Images is extremely important for finding optimal...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9761068/ |
| _version_ | 1811307051769397248 |
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| author | Kang Zhao Jingjing Liu Qingnan Wang Xianjun Wu Jihui Tu |
| author_facet | Kang Zhao Jingjing Liu Qingnan Wang Xianjun Wu Jihui Tu |
| author_sort | Kang Zhao |
| collection | DOAJ |
| description | Road is one of important traffic lifelines that could be damaged after disaster by landslide rubble, buildings debris, and collapsed branches of trees. Therefore, road damage detection and assessment using post-Disaster High-Resolution Remote Sensing Images is extremely important for finding optimal paths and conducting rescue missions. In an emergency context, the existing methods based on change detection for road damage detection are difficult to achieve due to the mismatch of different data sources, especially for rural areas where the pre-disaster remote sensing imagery are hard to obtain. In this paper, a novel method based on the Tracking, Learning, and Detector (TLD) framework for detecting the damaged road region from post-disaster high-resolution remote sensing image is presented. First, a spoke wheel operator is employed to define the initial template of road. Then, the TLD framework is used to identify the suspected road damaged areas. Finally, the damaged road areas are extracted by pruning the false damaged roads. The proposed method was evaluated using post-disaster high-resolution remote sensing images collected over Beichuan, China in 2008 and Lushan, China in 2013. The results show that the proposed method is feasible and effective for road damage detection and assessment. Our main conclusion is that such an approach qualifies for practical use. |
| first_indexed | 2024-04-13T08:57:32Z |
| format | Article |
| id | doaj.art-73160966c4474c19929f114850c35b4d |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-13T08:57:32Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-73160966c4474c19929f114850c35b4d2022-12-22T02:53:15ZengIEEEIEEE Access2169-35362022-01-0110435524356110.1109/ACCESS.2022.31690319761068Road Damage Detection From Post-Disaster High-Resolution Remote Sensing Images Based on TLD FrameworkKang Zhao0https://orcid.org/0000-0002-5373-6308Jingjing Liu1https://orcid.org/0000-0003-2579-8189Qingnan Wang2https://orcid.org/0000-0003-3012-2706Xianjun Wu3Jihui Tu4https://orcid.org/0000-0002-3432-6020National Engineering Laboratory for Surface Transportation Weather Impacts Prevention, Broadvision Engineering Consultants, Kunming, ChinaElectronics & Information School of Yangtze University, Jingzhou, ChinaCollege of Mechanical and Optoelectronic Physics, Huaihua University, Huaihua, ChinaPowerChina Kunming Engineering Corporation Limited, Kunming, ChinaNational Engineering Laboratory for Surface Transportation Weather Impacts Prevention, Broadvision Engineering Consultants, Kunming, ChinaRoad is one of important traffic lifelines that could be damaged after disaster by landslide rubble, buildings debris, and collapsed branches of trees. Therefore, road damage detection and assessment using post-Disaster High-Resolution Remote Sensing Images is extremely important for finding optimal paths and conducting rescue missions. In an emergency context, the existing methods based on change detection for road damage detection are difficult to achieve due to the mismatch of different data sources, especially for rural areas where the pre-disaster remote sensing imagery are hard to obtain. In this paper, a novel method based on the Tracking, Learning, and Detector (TLD) framework for detecting the damaged road region from post-disaster high-resolution remote sensing image is presented. First, a spoke wheel operator is employed to define the initial template of road. Then, the TLD framework is used to identify the suspected road damaged areas. Finally, the damaged road areas are extracted by pruning the false damaged roads. The proposed method was evaluated using post-disaster high-resolution remote sensing images collected over Beichuan, China in 2008 and Lushan, China in 2013. The results show that the proposed method is feasible and effective for road damage detection and assessment. Our main conclusion is that such an approach qualifies for practical use.https://ieeexplore.ieee.org/document/9761068/Road damaged detectionTLD modelLBPrandom forest (RF) |
| spellingShingle | Kang Zhao Jingjing Liu Qingnan Wang Xianjun Wu Jihui Tu Road Damage Detection From Post-Disaster High-Resolution Remote Sensing Images Based on TLD Framework IEEE Access Road damaged detection TLD model LBP random forest (RF) |
| title | Road Damage Detection From Post-Disaster High-Resolution Remote Sensing Images Based on TLD Framework |
| title_full | Road Damage Detection From Post-Disaster High-Resolution Remote Sensing Images Based on TLD Framework |
| title_fullStr | Road Damage Detection From Post-Disaster High-Resolution Remote Sensing Images Based on TLD Framework |
| title_full_unstemmed | Road Damage Detection From Post-Disaster High-Resolution Remote Sensing Images Based on TLD Framework |
| title_short | Road Damage Detection From Post-Disaster High-Resolution Remote Sensing Images Based on TLD Framework |
| title_sort | road damage detection from post disaster high resolution remote sensing images based on tld framework |
| topic | Road damaged detection TLD model LBP random forest (RF) |
| url | https://ieeexplore.ieee.org/document/9761068/ |
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