Flood Detection Based on Unmanned Aerial Vehicle System and Deep Learning
Floods are one of the main natural disasters, which cause huge damage to property, infrastructure, and economic losses every year. There is a need to develop an approach that could instantly detect flooded extent. Satellite remote sensing has been useful in emergency responses; however, with signifi...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2022/6155300 |
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| _version_ | 1832553579547721728 |
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| author | Kaixin Yang Sujie Zhang Xinran Yang Nan Wu |
| author_facet | Kaixin Yang Sujie Zhang Xinran Yang Nan Wu |
| author_sort | Kaixin Yang |
| collection | DOAJ |
| description | Floods are one of the main natural disasters, which cause huge damage to property, infrastructure, and economic losses every year. There is a need to develop an approach that could instantly detect flooded extent. Satellite remote sensing has been useful in emergency responses; however, with significant weakness due to long revisit period and unavailability during rainy/cloudy weather conditions. In recent years, unmanned aerial vehicle (UAV) systems have been widely used, especially in the fields of disaster monitoring and complex environments. This study employs deep learning models to develop an automated detection of flooded buildings with UAV aerial images. The method was explored in a case study for the Kangshan levee of Poyang Lake. Experimental results show that the inundation for the focal buildings and vegetation can be detected from the images with 88% and 85% accuracy, respectively. And further, we can estimate the buildings’ inundation area according to the UAV images and flight parameters. The result of this study shows promising value of the accuracy and timely visualization of the spatial distribution of inundation at the object level for the end users from flood emergency response sector. |
| format | Article |
| id | doaj-art-e47b76c79df54b9eafba94b909fdfcd6 |
| institution | Kabale University |
| issn | 1099-0526 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-e47b76c79df54b9eafba94b909fdfcd62025-02-03T05:53:49ZengWileyComplexity1099-05262022-01-01202210.1155/2022/6155300Flood Detection Based on Unmanned Aerial Vehicle System and Deep LearningKaixin Yang0Sujie Zhang1Xinran Yang2Nan Wu3Tianjin CollegeTianjin CollegeTianjin University of Science and TechnologyTianjin CollegeFloods are one of the main natural disasters, which cause huge damage to property, infrastructure, and economic losses every year. There is a need to develop an approach that could instantly detect flooded extent. Satellite remote sensing has been useful in emergency responses; however, with significant weakness due to long revisit period and unavailability during rainy/cloudy weather conditions. In recent years, unmanned aerial vehicle (UAV) systems have been widely used, especially in the fields of disaster monitoring and complex environments. This study employs deep learning models to develop an automated detection of flooded buildings with UAV aerial images. The method was explored in a case study for the Kangshan levee of Poyang Lake. Experimental results show that the inundation for the focal buildings and vegetation can be detected from the images with 88% and 85% accuracy, respectively. And further, we can estimate the buildings’ inundation area according to the UAV images and flight parameters. The result of this study shows promising value of the accuracy and timely visualization of the spatial distribution of inundation at the object level for the end users from flood emergency response sector.http://dx.doi.org/10.1155/2022/6155300 |
| spellingShingle | Kaixin Yang Sujie Zhang Xinran Yang Nan Wu Flood Detection Based on Unmanned Aerial Vehicle System and Deep Learning Complexity |
| title | Flood Detection Based on Unmanned Aerial Vehicle System and Deep Learning |
| title_full | Flood Detection Based on Unmanned Aerial Vehicle System and Deep Learning |
| title_fullStr | Flood Detection Based on Unmanned Aerial Vehicle System and Deep Learning |
| title_full_unstemmed | Flood Detection Based on Unmanned Aerial Vehicle System and Deep Learning |
| title_short | Flood Detection Based on Unmanned Aerial Vehicle System and Deep Learning |
| title_sort | flood detection based on unmanned aerial vehicle system and deep learning |
| url | http://dx.doi.org/10.1155/2022/6155300 |
| work_keys_str_mv | AT kaixinyang flooddetectionbasedonunmannedaerialvehiclesystemanddeeplearning AT sujiezhang flooddetectionbasedonunmannedaerialvehiclesystemanddeeplearning AT xinranyang flooddetectionbasedonunmannedaerialvehiclesystemanddeeplearning AT nanwu flooddetectionbasedonunmannedaerialvehiclesystemanddeeplearning |