Damage Assessment from Social Media Imagery Data During Disasters
Rapid access to situation-sensitive data through social media networks creates new opportunities to address a number of real-world problems. Damage assessment during disasters is a core situational awareness task for many humanitarian organizations that traditionally takes weeks and months. In this...
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| Veröffentlicht in: | 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) S. 569 - 576 |
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| Hauptverfasser: | , , , |
| Format: | Tagungsbericht |
| Sprache: | Englisch |
| Veröffentlicht: |
New York, NY, USA
ACM
31.07.2017
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| Schriftenreihe: | ACM Conferences |
| Schlagworte: |
Computing methodologies
> Artificial intelligence
> Natural language processing
> Information extraction
Human-centered computing
> Collaborative and social computing
> Collaborative and social computing systems and tools
Human-centered computing
> Collaborative and social computing
> Collaborative and social computing systems and tools
> Social networking sites
Human-centered computing
> Collaborative and social computing
> Collaborative and social computing theory, concepts and paradigms
|
| ISBN: | 1450349935, 9781450349932 |
| ISSN: | 2473-991X |
| Online-Zugang: | Volltext |
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| Abstract | Rapid access to situation-sensitive data through social media networks creates new opportunities to address a number of real-world problems. Damage assessment during disasters is a core situational awareness task for many humanitarian organizations that traditionally takes weeks and months. In this work, we analyze images posted on social media platforms during natural disasters to determine the level of damage caused by the disasters. We employ state-of-the-art machine learning techniques to perform an extensive experimentation of damage assessment using images from four major natural disasters. We show that the domain-specific fine-tuning of deep Convolutional Neural Networks (CNN) outperforms other state-of-the-art techniques such as Bag-of-Visual-Words (BoVW). High classification accuracy under both event-specific and cross-event test settings demonstrate that the proposed approach can effectively adapt deep-CNN features to identify the severity of destruction from social media images taken after a disaster strikes. |
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| AbstractList | Rapid access to situation-sensitive data through social media networks creates new opportunities to address a number of real-world problems. Damage assessment during disasters is a core situational awareness task for many humanitarian organizations that traditionally takes weeks and months. In this work, we analyze images posted on social media platforms during natural disasters to determine the level of damage caused by the disasters. We employ state-of-the-art machine learning techniques to perform an extensive experimentation of damage assessment using images from four major natural disasters. We show that the domain-specific fine-tuning of deep Convolutional Neural Networks (CNN) outperforms other state-of-the-art techniques such as Bag-of-Visual-Words (BoVW). High classification accuracy under both event-specific and cross-event test settings demonstrate that the proposed approach can effectively adapt deep-CNN features to identify the severity of destruction from social media images taken after a disaster strikes. |
| Author | Imran, Muhammad Nguyen, Dat T. Mitra, Prasenjit Ofli, Ferda |
| Author_xml | – sequence: 1 givenname: Dat T. surname: Nguyen fullname: Nguyen, Dat T. email: ndat@hbku.edu.qa organization: Qatar Computing Research Institute, HBKU, Doha, Qatar – sequence: 2 givenname: Ferda surname: Ofli fullname: Ofli, Ferda email: fofli@hbku.edu.qa organization: Qatar Computing Research Institute, HBKU, Doha, Qatar – sequence: 3 givenname: Muhammad surname: Imran fullname: Imran, Muhammad email: mimran@hbku.edu.qa organization: Qatar Computing Research Institute, HBKU, Doha, Qatar – sequence: 4 givenname: Prasenjit surname: Mitra fullname: Mitra, Prasenjit email: pmitra@psu.edu organization: The Pennsylvania State University, University Park, PA, USA |
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| Editor | Diesner, Jana Ferrari, Elena Xu, Guandong |
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| SubjectTerms | Computing methodologies Computing methodologies -- Artificial intelligence Computing methodologies -- Artificial intelligence -- Natural language processing Computing methodologies -- Artificial intelligence -- Natural language processing -- Information extraction Computing methodologies -- Machine learning Computing methodologies -- Machine learning -- Machine learning approaches Computing methodologies -- Machine learning -- Machine learning approaches -- Neural networks Human-centered computing Human-centered computing -- Collaborative and social computing Human-centered computing -- Collaborative and social computing -- Collaborative and social computing systems and tools Human-centered computing -- Collaborative and social computing -- Collaborative and social computing systems and tools -- Social networking sites Human-centered computing -- Collaborative and social computing -- Collaborative and social computing theory, concepts and paradigms Human-centered computing -- Collaborative and social computing -- Collaborative and social computing theory, concepts and paradigms -- Social media Information systems Information systems -- Information retrieval Information systems -- Information retrieval -- Retrieval tasks and goals Information systems -- Information retrieval -- Retrieval tasks and goals -- Sentiment analysis Information systems -- Information systems applications |
| Title | Damage Assessment from Social Media Imagery Data During Disasters |
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