Kashif Ahmad, M. Riegler, Ans Riaz, N. Conci, Duc-Tien Dang-Nguyen, P. Halvorsen
{"title":"The JORD System: Linking Sky and Social Multimedia Data to Natural Disasters","authors":"Kashif Ahmad, M. Riegler, Ans Riaz, N. Conci, Duc-Tien Dang-Nguyen, P. Halvorsen","doi":"10.1145/3078971.3079013","DOIUrl":null,"url":null,"abstract":"Being able to automatically link social media information and data to remote-sensed data holds large possibilities for society and research. In this paper, we present a system called JORD that is able to autonomously collect social media data about technological and environmental disasters, and link it automatically to remote-sensed data. In addition, we demonstrate that queries in local languages that are relevant to the exact position of natural disasters retrieve more accurate information about a disaster event. To show the capabilities of the system, we present some examples of disaster events detected by the system. To evaluate the quality of the provided information and usefulness of JORD from the potential users point of view we include a crowdsourced user study.","PeriodicalId":403556,"journal":{"name":"Proceedings of the 2017 ACM on International Conference on Multimedia Retrieval","volume":"106 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"12","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2017 ACM on International Conference on Multimedia Retrieval","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3078971.3079013","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 12
Abstract
Being able to automatically link social media information and data to remote-sensed data holds large possibilities for society and research. In this paper, we present a system called JORD that is able to autonomously collect social media data about technological and environmental disasters, and link it automatically to remote-sensed data. In addition, we demonstrate that queries in local languages that are relevant to the exact position of natural disasters retrieve more accurate information about a disaster event. To show the capabilities of the system, we present some examples of disaster events detected by the system. To evaluate the quality of the provided information and usefulness of JORD from the potential users point of view we include a crowdsourced user study.