Extraction of Urban Waterlogging Depth from Video Images Using Transfer Learning
文献类型：期刊论文
| 作者 | Jiang, Jingchao1,2; Liu, Junzhi3,4; Qin, Cheng-Zhi4,5 ; Wang, Dongliang6
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| 刊名 | WATER
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| 出版日期 | 2018-10-01 |
| 卷号 | 10期号:10页码:11 |
| 关键词 | urban waterlogging depth video image transfer learning lasso regression |
| ISSN号 | 2073-4441 |
| DOI | 10.3390/w10101485 |
| 通讯作者 | Liu, Junzhi(liujunzhi@njnu.edu.cn) |
| 英文摘要 | Urban flood control requires real-time and spatially detailed information regarding the waterlogging depth over large areas, but such information cannot be effectively obtained by the existing methods. Video supervision equipment, which is readily available in most cities, can record urban waterlogging processes in video form. These video data could be a valuable data source for waterlogging depth extraction. The present paper is aimed at demonstrating a new approach to extract urban waterlogging depths from video images based on transfer learning and lasso regression. First, a transfer learning model is used to extract feature vectors from a video image set of urban waterlogging. Second, a lasso regression model is trained with these feature vectors and employed to calculate the waterlogging depth. Two case studies in China were used to evaluate the proposed method, and the experimental results illustrate the effectiveness of the method. This method can be applied to video images from widespread cameras in cities, so that a powerful urban waterlogging monitoring network can be formed. |
| WOS关键词 | CONVOLUTIONAL NEURAL-NETWORKS ; LASSO |
| 资助项目 | National Natural Science Foundation of China[41601423] ; National Natural Science Foundation of China[41601413] ; Natural Science Foundation of Jiangsu Province of China[BK20150975] |
| WOS研究方向 | Water Resources |
| 语种 | 英语 |
| WOS记录号 | WOS:000451208400200 |
| 出版者 | MDPI |
| 资助机构 | National Natural Science Foundation of China ; Natural Science Foundation of Jiangsu Province of China |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/51556]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Liu, Junzhi |
| 作者单位 | 1.Hangzhou Dianzi Univ, Smart City Res Ctr, Hangzhou 310012, Zhejiang, Peoples R China 2.Smart City Collaborat Innovat Ctr Zhejiang Prov, Hangzhou 310012, Zhejiang, Peoples R China 3.Nanjing Normal Univ, Minist Educ, Key Lab Virtual Geog Environm, Nanjing 210023, Jiangsu, Peoples R China 4.Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Nanjing 210023, Jiangsu, Peoples R China 5.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China 6.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Land Surface Pattern & Simulat, Beijing 100101, Peoples R China |
| 推荐引用方式 GB/T 7714 | Jiang, Jingchao,Liu, Junzhi,Qin, Cheng-Zhi,et al. Extraction of Urban Waterlogging Depth from Video Images Using Transfer Learning[J]. WATER,2018,10(10):11. |
| APA | Jiang, Jingchao,Liu, Junzhi,Qin, Cheng-Zhi,&Wang, Dongliang.(2018).Extraction of Urban Waterlogging Depth from Video Images Using Transfer Learning.WATER,10(10),11. |
| MLA | Jiang, Jingchao,et al."Extraction of Urban Waterlogging Depth from Video Images Using Transfer Learning".WATER 10.10(2018):11. |
入库方式： OAI收割
来源：地理科学与资源研究所
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