Scalable flood level trend monitoring with surveillance cameras using a deep convolutional neural network
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Jan Dirk Wegner | Matthew Moy de Vitry | Simon Kramer | João P. Leitão | J. D. Wegner | J. Leitão | M. Moy de Vitry | Simon Kramer
[1] Henrik Madsen,et al. Comparison of the impacts of urban development and climate change on exposing European cities to pluvial flooding , 2017 .
[2] J. Seibert. Multi-criteria calibration of a conceptual runoff model using a genetic algorithm , 2000 .
[3] Andreas Scheidegger,et al. Parameter estimation of hydrologic models using a likelihood function for censored and binary observations. , 2017, Water research.
[4] Markus Disse,et al. Flood inundation forecasts using validation data generated with the assistance of computer vision , 2018, Journal of Hydroinformatics.
[5] C. Zevenbergen,et al. Urban pluvial flooding and stormwater management: A contemporary review of China’s challenges and “sponge cities” strategy , 2018 .
[6] André Paquier,et al. Modelling flash flood propagation in urban areas using a two-dimensional numerical model , 2009 .
[7] J. Seibert,et al. Information content of stream level class data for hydrological model calibration , 2017 .
[8] Nick van de Giesen,et al. Can urban pluvial flooding be predicted by open spatial data and weather data? , 2016, Environ. Model. Softw..
[9] Hidetomo Sakaino,et al. Camera-Vision-Based Water Level Estimation , 2016, IEEE Sensors Journal.
[10] Anke Xue,et al. Automatic Estimation of Urban Waterlogging Depths from Video Images Based on Ubiquitous Reference Objects , 2019, Remote. Sens..
[11] Yuan Wang,et al. Hyper-resolution monitoring of urban flooding with social media and crowdsourcing data , 2018, Comput. Geosci..
[12] Sebastian Ramos,et al. The Cityscapes Dataset , 2015, CVPR 2015.
[13] Kaiming He,et al. Mask R-CNN , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[14] C. Spearman. The proof and measurement of association between two things. By C. Spearman, 1904. , 1987, The American journal of psychology.
[15] W. Rauch,et al. Lost in calibration: why people still do not calibrate their models, and why they still should - a case study from urban drainage modelling. , 2016, Water science and technology : a journal of the International Association on Water Pollution Research.
[16] M. Levandowsky,et al. Distance between Sets , 1971, Nature.
[17] Dragan Savic,et al. Comparison of 1D/1D and 1D/2D Coupled (Sewer/Surface) Hydraulic Models for Urban Flood Simulation , 2009 .
[18] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[20] Jingchao Jiang,et al. Extraction of Urban Waterlogging Depth from Video Images Using Transfer Learning , 2018, Water.
[21] J. P. Leitão,et al. floodX: urban flash flood experiments monitored with conventional and alternative sensors , 2017 .
[22] P. Chaudhary,et al. FLOOD-WATER LEVEL ESTIMATION FROM SOCIAL MEDIA IMAGES , 2019, ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences.
[23] Youngjoon Han,et al. Embedded implementation of image-based water-level measurement system , 2011 .
[24] Kunihiko Fukushima,et al. Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position , 1980, Biological Cybernetics.
[25] P. Bates,et al. Benchmarking 2D hydraulic models for urban flooding , 2008 .
[26] Wes McKinney,et al. Data Structures for Statistical Computing in Python , 2010, SciPy.
[27] Min Liu,et al. Validating city-scale surface water flood modelling using crowd-sourced data , 2016 .
[28] Xiangliang Zhang,et al. Flash Flood Detection in Urban Cities Using Ultrasonic and Infrared Sensors , 2016, IEEE Sensors Journal.
[29] Oswaldo Morales-Nápoles,et al. Trends in flood losses in Europe over the past 150 years , 2018, Nature Communications.
[30] Kai Liu,et al. Developing an effective 2-D urban flood inundation model for city emergency management based on cellular automata , 2014 .
[31] Wei Gao,et al. Inundated Areas Extraction Based on Raindrop Photometric Model (RPM) in Surveillance Video , 2018, Water.
[32] Ron Kikinis,et al. Statistical validation of image segmentation quality based on a spatial overlap index. , 2004, Academic radiology.
[33] B. Sanders,et al. Calibration of stormwater management model using flood extent data , 2014 .
[34] Seunghoon Hong,et al. Learning Deconvolution Network for Semantic Segmentation , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).
[35] Thomas Brox,et al. U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.
[36] J.A.E. Ten Veldhuis,et al. How the choice of flood damage metrics influences urban flood risk assessment , 2011 .
[37] Graham W. Taylor,et al. Adaptive deconvolutional networks for mid and high level feature learning , 2011, 2011 International Conference on Computer Vision.
[38] Shi-Wei Lo,et al. Visual Sensing for Urban Flood Monitoring , 2015, Sensors.