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Laura Lopez-Fuentes; Joost Van de Weijer; Marc Bolaños; Harald Skinnemoen |
 
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| Title | Multi-modal Deep Learning Approach for Flood Detection | Type | Conference Article | |||
| Year | 2017 | Publication | MediaEval Benchmarking Initiative for Multimedia Evaluation | Abbreviated Journal | ||
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| Abstract | In this paper we propose a multi-modal deep learning approach to detect floods in social media posts. Social media posts normally contain some metadata and/or visual information, therefore in order to detect the floods we use this information. The model is based on a Convolutional Neural Network which extracts the visual features and a bidirectional Long Short-Term Memory network to extract the semantic features from the textual metadata. We validate the
method on images extracted from Flickr which contain both visual information and metadata and compare the results when using both, visual information only or metadata only. This work has been done in the context of the MediaEval Multimedia Satellite Task. |
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| Address | Dublin; Ireland; September 2017 | |||||
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| Area | Expedition | Conference | MediaEval | |||
| Notes | LAMP; 600.084; 600.109; 600.120;CIC | Approved | no | |||
| Call Number | Admin @ si @ LWB2017a | Serial | 2974 | |||
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