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Extraction of Pluvial Flood Relevant Volunteered Geographic Information (VGI) by Deep Learning from User Generated Texts and Photos

Institute of Cartography and Geoinformatics, Leibniz Universität Hannover, Appelstraße 9a, 30167 Hannover, Germany
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ISPRS Int. J. Geo-Inf. 2018, 7(2), 39; https://doi.org/10.3390/ijgi7020039
Received: 7 November 2017 / Revised: 17 January 2018 / Accepted: 21 January 2018 / Published: 25 January 2018
In recent years, pluvial floods caused by extreme rainfall events have occurred frequently. Especially in urban areas, they lead to serious damages and endanger the citizens’ safety. Therefore, real-time information about such events is desirable. With the increasing popularity of social media platforms, such as Twitter or Instagram, information provided by voluntary users becomes a valuable source for emergency response. Many applications have been built for disaster detection and flood mapping using crowdsourcing. Most of the applications so far have merely used keyword filtering or classical language processing methods to identify disaster relevant documents based on user generated texts. As the reliability of social media information is often under criticism, the precision of information retrieval plays a significant role for further analyses. Thus, in this paper, high quality eyewitnesses of rainfall and flooding events are retrieved from social media by applying deep learning approaches on user generated texts and photos. Subsequently, events are detected through spatiotemporal clustering and visualized together with these high quality eyewitnesses in a web map application. Analyses and case studies are conducted during flooding events in Paris, London and Berlin. View Full-Text
Keywords: social media; crowdsourcing; volunteered geographic information; multimedia information retrieval; convolutional neural network; transfer learning; word embedding; flood mapping social media; crowdsourcing; volunteered geographic information; multimedia information retrieval; convolutional neural network; transfer learning; word embedding; flood mapping
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Feng, Y.; Sester, M. Extraction of Pluvial Flood Relevant Volunteered Geographic Information (VGI) by Deep Learning from User Generated Texts and Photos. ISPRS Int. J. Geo-Inf. 2018, 7, 39.

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