IGSNRR OpenIR
Extraction of Urban Waterlogging Depth from Video Images Using Transfer Learning
Jiang, Jingchao1,2; Liu, Junzhi3,4; Qin, Cheng-Zhi4,5; Wang, Dongliang6
2018-10-01
Source PublicationWATER
ISSN2073-4441
Volume10Issue:10Pages:11
Corresponding AuthorLiu, Junzhi(liujunzhi@njnu.edu.cn)
AbstractUrban 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.
Keywordurban waterlogging depth video image transfer learning lasso regression
DOI10.3390/w10101485
WOS KeywordCONVOLUTIONAL NEURAL-NETWORKS ; LASSO
Indexed BySCI
Language英语
Funding ProjectNational Natural Science Foundation of China[41601423] ; National Natural Science Foundation of China[41601413] ; Natural Science Foundation of Jiangsu Province of China[BK20150975]
Funding OrganizationNational Natural Science Foundation of China ; Natural Science Foundation of Jiangsu Province of China
WOS Research AreaWater Resources
WOS SubjectWater Resources
WOS IDWOS:000451208400200
PublisherMDPI
Citation statistics
Document Type期刊论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/51557
Collection中国科学院地理科学与资源研究所
Corresponding AuthorLiu, Junzhi
Affiliation1.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
Recommended Citation
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.
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