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Combining human computing and machine learning to make sense of big (aerial) data for disaster response

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dc.contributor.author Ofli, Ferda
dc.contributor.author Meier, Patrick
dc.contributor.author Imran, Muhammad
dc.contributor.author Castillo, Carlos
dc.contributor.author Tuia, Devis
dc.contributor.author Rey, Nicolas
dc.contributor.author Briant, Julien
dc.contributor.author Millet, Pauline
dc.contributor.author Reinhard, Friedrich
dc.contributor.author Parkan, Matthew
dc.contributor.author Joost, Stéphane
dc.date.accessioned 2019-03-21T13:45:00Z
dc.date.available 2019-03-21T13:45:00Z
dc.date.issued 2016
dc.identifier.citation Ofli F, Meier P, Imran M, Castillo C, Tuia D, Rey N, Briant J, Millet P, Reinhard F, Parkan M, Joost S. Combining human computing and machine learning to make sense of big (aerial) data for disaster response. Big Data. 2016 Mar;4(1):47-59. DOI: 10.1089/big.2014.0064
dc.identifier.issn 2167-6461
dc.identifier.uri http://hdl.handle.net/10230/36882
dc.description.abstract Aerial imagery captured via unmanned aerial vehicles (UAVs) is playing an increasingly important role in disaster response. Unlike satellite imagery, aerial imagery can be captured and processed within hours rather than days. In addition, the spatial resolution of aerial imagery is an order of magnitude higher than the imagery produced by the most sophisticated commercial satellites today. Both the United States Federal Emergency Management Agency (FEMA) and the European Commission’s Joint Research Center ( JRC) have noted that aerial imagery will inevitably present a big data challenge. The purpose of this article is to get ahead of this future challenge by proposing a hybrid crowdsourcing and real-time machine learning solution to rapidly process large volumes of aerial data for disaster response in a time-sensitive manner. Crowdsourcing can be used to annotate features of interest in aerial images (such as damaged shelters and roads blocked by debris). These human-annotated features can then be used to train a supervised machine learning system to learn to recognize such features in new unseen images. In this article, we describe how this hybrid solution for image analysis can be implemented as a module (i.e., Aerial Clicker) to extend an existing platform called Artificial Intelligence for Disaster Response (AIDR), which has already been deployed to classify microblog messages during disasters using its Text Clicker module and in response to Cyclone Pam, a category 5 cyclone that devastated Vanuatu in March 2015. The hybrid solution we present can be applied to both aerial and satellite imagery and has applications beyond disaster response such as wildlife protection, human rights, and archeological exploration. As a proof of concept, we recently piloted this solution using very high-resolution aerial photographs of a wildlife reserve in Namibia to support rangers with their wildlife conservation efforts (SAVMAP project, http://lasig.epfl.ch/savmap). The results suggest that the platform we have developed to combine crowdsourcing and machine learning to make sense of large volumes of aerial images can be used for disaster response.
dc.format.mimetype application/pdf
dc.language.iso eng
dc.publisher Mary Ann Liebert, Inc
dc.relation.ispartof Combining human computing and machine learning to make sense of big (aerial) data for disaster response. Big Data. 2016 Mar;4(1):47-59
dc.rights Final publication is available from Mary Ann Liebert, Inc., publishers http://dx.doi.org/10.1089/big.2014.0064
dc.title Combining human computing and machine learning to make sense of big (aerial) data for disaster response
dc.type info:eu-repo/semantics/article
dc.identifier.doi http://dx.doi.org/10.1089/big.2014.0064
dc.subject.keyword Big data analytics
dc.subject.keyword Crowdsourcing
dc.subject.keyword Machine learning
dc.subject.keyword Remote sensing
dc.subject.keyword UAV
dc.rights.accessRights info:eu-repo/semantics/openAccess
dc.type.version info:eu-repo/semantics/acceptedVersion

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