Autor/a:
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Ofli, Ferda; Meier, Patrick; Imran, Muhammad; Castillo, Carlos; Tuia, Devis; Rey, Nicolas; Briant, Julien; Millet, Pauline; Reinhard, Friedrich; Parkan, Matthew; Joost, Stéphane
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Abstract:
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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. |