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Detection of Flooding Events in Social Multimedia and Satellite Imagery using Deep Neural Networks

Benjamin Bischke, Prakriti Bhardwaj, Aman Gautam, Patrick Helber, Damian Borth, Andreas Dengel
2017 MediaEval Benchmarking Initiative for Multimedia Evaluation  
Our results show that IR information is of vital importance for the detection of flooded areas in satellite imagery.  ...  The results show that the fusion of visual and textual features extracted by deep networks can be effectively used to retrieve social multimedia reports which provide a directed evidence of flooding.  ...  Flood Detection in Satellite Imagery In this section, we explain our approach for the segmentation of flooded areas in satellite images using deep neural networks. 1.2.1 Pre-Processing.  ... 
dblp:conf/mediaeval/BischkeBGHBD17 fatcat:f7hzy6mtdnevha4rm7ieqqn4r4

Deep Learning Approaches for Flood Classification and Flood Aftermath Detection

Naina Said, Konstantin Pogorelov, Kashif Ahmad, Michael Riegler, Nasir Ahmad, Olga Ostroukhova, Pål Halvorsen, Nicola Conci
2018 MediaEval Benchmarking Initiative for Multimedia Evaluation  
This paper presents the method proposed by team UTAOS for MediaEval 2018 Multimedia Satellite Task: Emergency Response for Flooding Events.  ...  For the second challenge, we rely on a convolutional neural networks (CNNs) and a transfer learning-based classification approach achieving an average F1-score of 62.30% and 61.02% for run 1 and run 2,  ...  The challenge is composed of two parts, namely (i) flood classification for social multimedia and (ii) flood detection in satellite imagery.  ... 
dblp:conf/mediaeval/SaidPARAOHC18 fatcat:76yg4psqszgm3cxu5wintcrtvu

Multimedia Analysis Techniques for Flood Detection Using Images, Articles and Satellite Imagery

Stelios Andreadis, Marios Bakratsas, Panagiotis Giannakeris, Anastasia Moumtzidou, Ilias Gialampoukidis, Stefanos Vrochidis, Ioannis Kompatsiaris
2019 Zenodo  
Deep Convolutional Neural Networks were deployed to classify articles as flood event-related based on their images, but also to detect flooding events in satellite sequences.  ...  This paper presents the various algorithms that the CERTH-ITI team has implemented to tackle three tasks that relate to the problem of flood severity estimation, using satellite images and online media  ...  If the output of the two SVM binary classifiers coincide, City-Centered Satellite Sequences (CCSS) The first approach to detect flood events using satellite sequences involved the use of a deep learning  ... 
doi:10.5281/zenodo.4282227 fatcat:5sxr67sk6jafzobc2fzbsbj4fu

Natural Disasters Detection in Social Media and Satellite imagery: a survey [article]

Naina Said, Kashif Ahmad, Michael Regular, Konstantin Pogorelov, Laiq Hassan, Nasir Ahmad, Nicola Conci
2019 arXiv   pre-print
of disaster-related visual content from social media; and (iii) disaster detection in satellite imagery.  ...  In this paper, we survey the existing literature on disaster detection and analysis of the retrieved information from social media and satellites.  ...  and videos) available in social media, and detection of disaster's affected areas in satellite imagery.  ... 
arXiv:1901.04277v1 fatcat:5zidbp3owbe6ld33pt4j3aijtq

A multimodal approach in estimating road passability through a flooded area using social media and satellite images

Anastasia Moumtzidou, Panagiotis Giannakeris, Stelios Andreadis, Athanasios Mavropoulos, Georgios Meditskos, Ilias Gialampoukidis, Konstantinos Avgerinakis, Stefanos Vrochidis, Ioannis Kompatsiaris
2018 Zenodo  
and more meaningful understanding of the flood events.  ...  This paper presents the algorithms that CERTH-ITI team deployed so as to deal with flood detection and road passability from social media and satellite data.  ...  ACKNOWLEDGMENTS This work was supported by EC-funded projects H2020-700475-beAWARE and H2020-776019-EOPEN.  ... 
doi:10.5281/zenodo.2540397 fatcat:irwtba5ccbbsff5p52xjcambcy

Automatic detection of passable roads after floods in remote sensed and social media data [article]

Kashif Ahmad, Konstantin Pogorelov, Michael Riegler, Olga Ostroukhova, Paal Halvorsen, Nicola Conci, Rozenn Dahyot
2019 arXiv   pre-print
This paper addresses the problem of floods classification and floods aftermath detection utilizing both social media and satellite imagery.  ...  To identify whether or not it is possible for a vehicle to pass a road in satellite images, we rely on Convolutional Neural Networks and a transfer learning-based classification approach.  ...  Acknowledgments This research is partly supported by the ADAPT Centre for Digital Content Technology, which is funded under the Science Foundation Ireland Research Centres Programme (Grant 13/RC/2106) and  ... 
arXiv:1901.03298v1 fatcat:ts7dwfsjmjav5n5ksmizt2rlfe

The need for training and benchmark datasets for convolutional neural networks in flood applications

Abdou Khouakhi, Joanna Zawadzka, Ian Truckell
2022 Hydrology Research  
of modern convolutional neural networks (CNNs) to specific flood-related problems such as flood extent detection and flood depth estimation.  ...  Flood-related image datasets from social media, smartphones, CCTV cameras, and unmanned aerial vehicles (UAVs) present valuable data for the management of flood risk, and particularly for the application  ...  Combination of satellite and social media images and Satellite task 2017 Social Media (2017) segmentation masks. MediaEval Multimedia Satellite Imagery and Bischke et al.  ... 
doi:10.2166/nh.2022.093 fatcat:h3ct5oiyvba6hikumst6toh6gi

Deep Learning Models for Estimation of Flood Severity Using Multimodal and Satellite Images

Hariny Ganapathy, Geetika Bandlamudi, Yamini L, Bhuvana J, T. T. Mirnalinee
2019 MediaEval Benchmarking Initiative for Multimedia Evaluation  
Satellite images provide variety of information like weather, how any event on land unfolds and hence they play an important role in disaster management.  ...  This paper addresses the Multimedia Satellite Task at Media-Eval 2019. We have focussed on the challenge of extracting information present in satellite images.  ...  Pre-trained networks like DeepSentiBank have also been used to detect floods with the help of social multimedia and satellite imagery and are proven to be giving good results to detect floods [1] .  ... 
dblp:conf/mediaeval/GanapathyBLJM19 fatcat:fqx7ntqh55gcpaiwuspipn7aum

AI-Based Flood Event Understanding and Quantifying Using Online Media and Satellite Data

Mirko Zaffaroni, Laura Lopez-Fuentes, Alessandro Farasin, Paolo Garza, Harald Skinnemoen
2019 MediaEval Benchmarking Initiative for Multimedia Evaluation  
In this paper we study the problem of flood detection and quantification using online media and satellite data.  ...  We present a three approaches, two of them based on neural networks and a third one based on the combination of different bands of satellite images.  ...  ACKNOWLEDGMENTS This work was supported by the European Commission H2020 SHELTER project, GA no. 821282 and by the Spanish grant TIN2016-75404-P. Laura Lopez-Fuentes benefits from the NAERINGSPHD  ... 
dblp:conf/mediaeval/ZaffaroniLFGS19 fatcat:ut3e4ydjfbdvzaynqw3fnxpisa

Multi-Modal Machine Learning for Flood Detection in News, Social Media and Satellite Sequences [article]

Kashif Ahmad, Konstantin Pogorelov, Mohib Ullah, Michael Riegler, Nicola Conci, Johannes Langguth, Ala Al-Fuqaha
2019 arXiv   pre-print
Finally, for the flooding detection in time-based satellite image sequences we used a combination of classicalcomputer-vision and machine learning approaches achieving anaverage F1-score of58.82%  ...  In this paper we present our methods for the MediaEval 2019 Mul-timedia Satellite Task, which is aiming to extract complementaryinformation associated with adverse events from Social Media andsatellites  ...  in social media content (MFLE) and predicting a flood event in a set of sequences of satellite images of a certain city over a certain length of time (CSS).  ... 
arXiv:1910.02932v1 fatcat:i7xensrjfbdgtbmilrj32x3mpu

Convolutional Neural Networks for Disaster Images Retrieval

Sheharyar Ahmad, Kashif Ahmad, Nasir Ahmad, Nicola Conci
2017 MediaEval Benchmarking Initiative for Multimedia Evaluation  
This paper presents the method proposed by MRLDCSE team for the disaster image retrieval task in Mediaeval 2017 challenge on Multimedia and Satellite.  ...  In the proposed work, for visual information, we rely on Convolutional Neural Networks (CNN) features extracted with two different models pre-trained on ImageNet and places datasets.  ...  The proposed challenge is composed of two sub-tasks namely (i) Disaster Image Retrieval from Social Media (DIRSM) and (ii) Flood Detection in Satellite Images (FDSI).  ... 
dblp:conf/mediaeval/AhmadAAC17 fatcat:zwnjedqfbrdcbgsk566hxrf4om

Flood Detection from Social Multimedia and Satellite Images Using Ensemble and Transfer Learning with CNN Architectures

Danielle Dias, Ulisses Dias
2018 MediaEval Benchmarking Initiative for Multimedia Evaluation  
In this paper we explore deep convolutional neural networks pretrained on ImageNet along with transfer learning mechanism to detect if an area has been a ected by a ood in terms of access.  ...  The second dataset contains high resolution satellite imagery of partially ooded areas and the goal is to identify sections of roads that are potentially blocked.  ...  ACKNOWLEDGMENTS We thank CAPES and CNPq (grant 400487/2016-0) and FAPESP (grant 2015/11937-9).  ... 
dblp:conf/mediaeval/DiasD18a fatcat:4vguugn45zbcrhefeqwnxxjotm

FLOOD DETECTION IN TIME SERIES OF OPTICAL AND SAR IMAGES

C. Rambour, N. Audebert, E. Koeniguer, B. Le Saux, M. Crucianu, M. Datcu
2020 The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences  
To help the community go forward, we introduce a new dataset composed of co-registered optical and SAR images time series for the detection of flood events and new neural network approaches to leverage  ...  Moreover, various acquisition techniques such as Synthetic Aperture Radar (SAR) can also be used for problems that could not be tackled only through optical images.  ...  For example, in MediaEval 2017, (Bischke et al., 2017) learned deep models to perform flood detection in natural images using ancillary data from social networks.  ... 
doi:10.5194/isprs-archives-xliii-b2-2020-1343-2020 fatcat:q4zlzo6wlndczhmmxj75yrfwzi

Processing Social Media Messages in Mass Emergency

Muhammad Imran, Carlos Castillo, Fernando Diaz, Sarah Vieweg
2018 Companion of the The Web Conference 2018 on The Web Conference 2018 - WWW '18  
Millions of people use social media to share information during disasters and mass emergencies.  ...  Information available on social media, particularly in the early hours of an event when few other sources are available, can be extremely valuable for emergency responders and decision makers, helping  ...  Deep Neural Networks (DNNs) use distributed condensed representation of words and learn higher level abstract features automatically.  ... 
doi:10.1145/3184558.3186242 dblp:conf/www/000200V18 fatcat:qccrrnreaffhlptqc6vtkdhmki

BMC@MediaEval 2017 Multimedia Satellite Task via Regression Random Forest

Xiyao Fu, Yi Bin, Liang Peng, Jie Zhou, Yang Yang, Heng Tao Shen
2017 MediaEval Benchmarking Initiative for Multimedia Evaluation  
The 2017 Multimedia Satellite Task consists of two subtasks: Disaster Image Retrieval from Social Media (DIRSM) task and Flood-Detection in Satellite Images(FDSI) task.  ...  INTRODUCTION The outburst of social media provides us with an opportunity to deal with speci c tasks, e.g., disaster prediction and speci c scene identi cation.  ...  In recent years, Convolutional Neural Networks (CNNs) has been dominating in the eld of computer vision, such as recognition and detection.  ... 
dblp:conf/mediaeval/FuBPZ0S17 fatcat:wfierswktfahdhxkb4x27m3iyy
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