|Authors||K. Ahmad, K. Pogorelov, M. Riegler, N. Conci and P. Halvorsen|
|Title||Social media and satellites|
|Afilliation||Communication Systems, Machine Learning|
|Publication Type||Journal Article|
|Year of Publication||2018|
|Journal||Multimedia Tools and Applications|
Being able to automatically link social media and satellite imagery holds large opportunities for research, with a potentially considerable impact on society. The possibility of integrating different information sources opens in fact to new scenarios where the wide coverage of satellite imaging can be used as a collector of the fine-grained details provided by the social media. Remote-sensed data and social media data can well complement each other, integrating the wide perspective provided by the satellite view with the information collected locally, being it textual, audio, or visual. Among the possible applications, natural disasters are certainly one of the most interesting scenarios, where global and local perspectives are needed at the same time. In this paper, we present a system called JORD that is able to autonomously collect social media data (including the text analysis in local languages) about technological and environmental disasters, and link it automatically to remote-sensed data. Moreover, in order to ensure the quality of retrieved information, JORD is equipped with a hierarchical filtering mechanism relying on the temporal information and the content analysis of retrieved multimedia data. To show the capabilities of the system, we present a large number of disaster events detected by the system, and we evaluate both the quality of the provided information about the events and the usefulness of JORD from potential users viewpoint, using crowdsourcing.