Assessing flood severity from georeferenced photos

The use of georeferenced social media data in disaster and crisis management is increasing rapidly. Particularly in connection to flooding events, georeferenced images shared by citizens can provide situational awareness to emergency responders, as well as assistance to financial loss assessment, giving information that would otherwise be very hard to collect through conventional sensors or remote sensing products. Moreover, recent advances in computer vision and deep learning can perhaps support the automated analysis of these data. In this paper, focusing on ground-level images taken by humans during flooding events, we evaluate the use of deep convolutional neural networks for (i) discriminating images showing direct evidence of a flood, and (ii) estimating the severity of the flooding event. Considering distinct datasets (i.e., the European Flood 2013 dataset, and data from different editions of the MediaEval Multimedia Satellite Task), we specifically evaluated models based on the DenseNet and EfficientNet neural network architectures, concluding that these models for image classification can achieve a very high accuracy on both tasks.

[1]  Li Fei-Fei,et al.  ImageNet: A large-scale hierarchical image database , 2009, CVPR.

[2]  Michael Riegler,et al.  CNN and GAN Based Satellite and Social Media Data Fusion for Disaster Detection , 2017, MediaEval.

[3]  Yi Yang,et al.  Diagnose like a Radiologist: Attention Guided Convolutional Neural Network for Thorax Disease Classification , 2018, ArXiv.

[4]  M. Geetha,et al.  Detection and estimation of the extent of flood from crowd sourced images , 2017, 2017 International Conference on Communication and Signal Processing (ICCSP).

[5]  Kilian Q. Weinberger,et al.  Densely Connected Convolutional Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[6]  Kai Liu,et al.  Developing an effective 2-D urban flood inundation model for city emergency management based on cellular automata , 2014 .

[7]  Linan Zhang,et al.  Forward Stability of ResNet and Its Variants , 2018, Journal of Mathematical Imaging and Vision.

[8]  Enhua Wu,et al.  Squeeze-and-Excitation Networks , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[9]  Lin Li,et al.  Data-Driven Flood Detection using Neural Networks , 2017, MediaEval.

[10]  Franccois Fleuret,et al.  Processing Megapixel Images with Deep Attention-Sampling Models , 2019, ICML.

[11]  Abien Fred Agarap.  Deep Learning using Rectified Linear Units (ReLU) , 2018, ArXiv.

[12]  Arkaitz Zubiaga,et al.  WISC at MediaEval 2017: Multimedia Satellite Task , 2017, MediaEval.

[13]  Claudio Rossi,et al.  River segmentation for flood monitoring , 2017, 2017 IEEE International Conference on Big Data (Big Data).

[14]  François Chollet,et al.  Xception: Deep Learning with Depthwise Separable Convolutions , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[15]  Yiannis Kompatsiaris,et al.  Visual and Textual Analysis of Social Media and Satellite Images for Flood Detection @ Multimedia Satellite Task MediaEval 2017 , 2017, MediaEval.

[16]  Leslie N. Smith,et al.  Cyclical Learning Rates for Training Neural Networks , 2015, 2017 IEEE Winter Conference on Applications of Computer Vision (WACV).

[17]  Shi-Wei Lo,et al.  Visual Sensing for Urban Flood Monitoring , 2015, Sensors.

[18]  Mark Sandler,et al.  MobileNetV2: Inverted Residuals and Linear Bottlenecks , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[19]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[20]  Yang Yang,et al.  BMC@MediaEval 2017 Multimedia Satellite Task via Regression Random Forest , 2017, MediaEval.

[21]  Kaiming He,et al.  Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[22]  Quoc V. Le,et al.  EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks , 2019, ICML.

[23]  Bo Chen,et al.  MnasNet: Platform-Aware Neural Architecture Search for Mobile , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[24]  Joost van de Weijer,et al.  Multi-modal Deep Learning Approach for Flood Detection , 2017, MediaEval.

[25]  Yiannis Kompatsiaris,et al.  People and Vehicles in Danger - A Fire and Flood Detection System in Social Media , 2018, 2018 IEEE 13th Image, Video, and Multidimensional Signal Processing Workshop (IVMSP).

[26]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[27]  Peter Salamon,et al.  Integrating Social Media into a Pan-European Flood Awareness System: A Multilingual Approach , 2019, ISCRAM.

[28]  Mohammed Bennamoun,et al.  A Guide to Convolutional Neural Networks for Computer Vision , 2018, A Guide to Convolutional Neural Networks for Computer Vision.

[29]  M. I. Elbakary,et al.  Floodwater detection on roadways from crowdsourced images , 2018, Comput. methods Biomech. Biomed. Eng. Imaging Vis..

[30]  Linda See,et al.  A Review of Citizen Science and Crowdsourcing in Applications of Pluvial Flooding , 2019, Front. Earth Sci..

[31]  Andreas Dengel,et al.  The Multimedia Satellite Task at MediaEval 2018 , 2017, MediaEval.

[32]  Andreas Dengel,et al.  Detection of Flooding Events in Social Multimedia and Satellite Imagery using Deep Neural Networks , 2017, MediaEval.

[33]  Zhi Zhang,et al.  Bag of Tricks for Image Classification with Convolutional Neural Networks , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[34]  Sethuraman N. Rao,et al.  A novel approach to urban flood monitoring using computer vision , 2014, Fifth International Conference on Computing, Communications and Networking Technologies (ICCCNT).