{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,3]],"date-time":"2026-03-03T17:07:22Z","timestamp":1772557642403,"version":"3.50.1"},"reference-count":22,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2019,3,11]],"date-time":"2019-03-11T00:00:00Z","timestamp":1552262400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41601423"],"award-info":[{"award-number":["41601423"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41601413"],"award-info":[{"award-number":["41601413"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Basic Research Program of China","doi-asserted-by":"publisher","award":["2015CB954102"],"award-info":[{"award-number":["2015CB954102"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Video supervision equipment, which is readily available in most cities, can record the processes of urban floods in video form. Ubiquitous reference objects, which often appear in videos, can be used to indicate urban waterlogging depths. This makes video images a valuable data source for obtaining waterlogging depths. However, the urban waterlogging information contained in video images has not been effectively mined and utilized. In this paper, we present a method to automatically estimate urban waterlogging depths from video images based on ubiquitous reference objects. First, reference objects from video images are detected during the flooding and non-flooding periods using an object detection model with a convolutional neural network (CNN). Then, waterlogging depths are estimated using the height differences between the detected reference objects in these two periods. A case study is used to evaluate the proposed method. The results show that our proposed method could effectively mine and utilize urban waterlogging depth information from video images. This method has the advantages of low economic cost, acceptable accuracy, high spatiotemporal resolution, and wide coverage. It is feasible to promote this proposed method within cities to monitor urban floods.<\/jats:p>","DOI":"10.3390\/rs11050587","type":"journal-article","created":{"date-parts":[[2019,3,12]],"date-time":"2019-03-12T03:49:31Z","timestamp":1552362571000},"page":"587","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":37,"title":["Automatic Estimation of Urban Waterlogging Depths from Video Images Based on Ubiquitous Reference Objects"],"prefix":"10.3390","volume":"11","author":[{"given":"Jingchao","family":"Jiang","sequence":"first","affiliation":[{"name":"Smart City Research Center, Hangzhou Dianzi University, Hangzhou 310012, China"},{"name":"Smart City Collaborative Innovation Center of Zhejiang Province, Hangzhou 310012, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7354-4207","authenticated-orcid":false,"given":"Junzhi","family":"Liu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Virtual Geographic Environment, Ministry of Education, Nanjing Normal University, Nanjing 210023, China"},{"name":"Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China"}]},{"given":"Changxiu","family":"Cheng","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing 100875, China"},{"name":"Key Laboratory of Environmental Change and Natural Disaster, Beijing Normal University, Beijing 100875, China"}]},{"given":"Jingzhou","family":"Huang","sequence":"additional","affiliation":[{"name":"Smart City Research Center, Hangzhou Dianzi University, Hangzhou 310012, China"},{"name":"Smart City Collaborative Innovation Center of Zhejiang Province, Hangzhou 310012, China"}]},{"given":"Anke","family":"Xue","sequence":"additional","affiliation":[{"name":"Smart City Research Center, Hangzhou Dianzi University, Hangzhou 310012, China"},{"name":"Smart City Collaborative Innovation Center of Zhejiang Province, Hangzhou 310012, China"}]}],"member":"1968","published-online":{"date-parts":[[2019,3,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1016\/j.envsoft.2017.06.027","article-title":"An integrated assessment of urban flooding mitigation strategies for robust decision making","volume":"95","author":"Xie","year":"2017","journal-title":"Environ. Modell. Softw."},{"key":"ref_2","first-page":"345","article-title":"The impacts of urbanisation and climate change on urban flooding and urban water quality: A review of the evidence concerning the United Kingdom","volume":"12","author":"Miller","year":"2017","journal-title":"J. Hydrol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1016\/j.cageo.2017.11.008","article-title":"Hyper-resolution monitoring of urban flooding with social media and crowdsourcing data","volume":"111","author":"Wang","year":"2018","journal-title":"Comput. Geosci."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Jiang, J., Liu, J., Qin, C.Z., and Wang, D. (2018). Extraction of urban waterlogging depth from video images using transfer learning. Water, 10.","DOI":"10.3390\/w10101485"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"4656","DOI":"10.3390\/s110504656","article-title":"Soft water level sensors for characterizing the hydrological behaviour of agricultural catchments","volume":"11","author":"Crabit","year":"2011","journal-title":"Sensors"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1437","DOI":"10.3390\/w7041437","article-title":"Urban flood mapping based on unmanned aerial vehicle remote sensing and random forest classifier\u2014A case of Yuyao, China","volume":"7","author":"Feng","year":"2015","journal-title":"Water"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"4005","DOI":"10.5194\/hess-20-4005-2016","article-title":"Advances in flash flood monitoring using unmanned aerial vehicles (UAVs)","volume":"20","author":"Perks","year":"2016","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"11791","DOI":"10.3390\/rs61211791","article-title":"The strengths and limitations in using the daily MODIS open water likelihood algorithm for identifying flood events","volume":"6","author":"Ticehurst","year":"2014","journal-title":"Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"17013","DOI":"10.3390\/rs71215871","article-title":"Preface: Remote sensing in flood monitoring and management","volume":"7","author":"Schumann","year":"2015","journal-title":"Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1007\/s11069-017-2755-0","article-title":"Rapid flood inundation mapping using social media, remote sensing and topographic data","volume":"87","author":"Rosser","year":"2017","journal-title":"Nat. Hazards"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2725","DOI":"10.5194\/nhess-15-2725-2015","article-title":"Social media as an information source for rapid flood inundation mapping","volume":"15","author":"Fohringer","year":"2015","journal-title":"Nat. Hazards Earth Syst. Sci."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"381","DOI":"10.5194\/nhess-15-381-2015","article-title":"Developing an effective 2-d urban flood inundation model for city emergency management based on cellular automata","volume":"15","author":"Liu","year":"2015","journal-title":"Nat. Hazards Earth Syst. Sci."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.envsoft.2018.06.010","article-title":"An integrated framework for high-resolution urban flood modelling considering multiple information sources and urban features","volume":"107","author":"Wang","year":"2018","journal-title":"Environ. Modell. Softw."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"2281","DOI":"10.2166\/wst.2009.659","article-title":"Vision-based system for the control and measurement of wastewater flow rate in sewer systems","volume":"60","author":"Nguyen","year":"2009","journal-title":"Water Sci. Technol."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"178","DOI":"10.1016\/j.jhydrol.2013.05.011","article-title":"Source and magnitude of error in an inexpensive image-based water level measurement system","volume":"496","author":"Gilmore","year":"2013","journal-title":"J. Hydrol."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1023\/B:VISI.0000029664.99615.94","article-title":"Distinctive image features from scale-invariant keypoints","volume":"60","author":"Lowe","year":"2004","journal-title":"Int. J. Comput. Vis."},{"key":"ref_17","unstructured":"Dalal, N., and Triggs, B. (2005, January 20\u201325). Histograms of oriented gradients for human detection. Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Diego, CA, USA."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Girshick, R., Donahue, J., Darrell, T., and Malik, J. (2014, January 23\u201328). Rich feature hierarchies for accurate object detection and semantic segmentation. Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.81"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.Y., and Berg, A.C. (2016, January 11\u201314). SSD: Single shot multibox detector. Proceedings of the Computer Vision\u2013European conference on computer vision 2016, Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","article-title":"Faster R-CNN: Towards real-time object detection with region proposal networks","volume":"39","author":"Ren","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_21","unstructured":"Dai, J., Li, Y., He, K., and Sun, J. (2016, January 5\u201310). R-FCN: Object detection via region-based fully convolutional networks. Proceedings of the 30th Conference on Neural Information Processing Systems, Barcelona, Spain."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016, January 27\u201330). You only look once: Unified, real-time object detection. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.91"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/5\/587\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T12:37:58Z","timestamp":1760186278000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/5\/587"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,3,11]]},"references-count":22,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2019,3]]}},"alternative-id":["rs11050587"],"URL":"https:\/\/doi.org\/10.3390\/rs11050587","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,3,11]]}}}