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Article

An AI-Enabled Framework for Real-Time Generation of News Articles Based on Big EO Data for Disaster Reporting

Centre for Research and Technology Hellas, Information Technologies Institute, 57001 Thessaloniki, Greece
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Academic Editor: Gyu Myoung Lee
Future Internet 2021, 13(6), 161; https://doi.org/10.3390/fi13060161
Received: 12 May 2021 / Revised: 31 May 2021 / Accepted: 17 June 2021 / Published: 19 June 2021
(This article belongs to the Special Issue Theory and Applications of Web 3.0 in the Media Sector)
In the field of journalism, the collection and processing of information from different heterogeneous sources are difficult and time-consuming processes. In the context of the theory of journalism 3.0, where multimedia data can be extracted from different sources on the web, the possibility of creating a tool for the exploitation of Earth observation (EO) data, especially images by professionals belonging to the field of journalism, is explored. With the production of massive volumes of EO image data, the problem of their exploitation and dissemination to the public, specifically, by professionals in the media industry, arises. In particular, the exploitation of satellite image data from existing tools is difficult for professionals who are not familiar with image processing. In this scope, this article presents a new innovative platform that automates some of the journalistic practices. This platform includes several mechanisms allowing users to early detect and receive information about breaking news in real-time, retrieve EO Sentinel-2 images upon request for a certain event, and automatically generate a personalized article according to the writing style of the author. Through this platform, the journalists or editors can also make any modifications to the generated article before publishing. This platform is an added-value tool not only for journalists and the media industry but also for freelancers and article writers who use information extracted from EO data in their articles. View Full-Text
Keywords: web 3.0; article composition; Earth observation (EO); journalism 3.0; media industry; journalistic workflow; journalistic practices; text generation with artificial intelligence (AI); disaster reporting; EarthPress web 3.0; article composition; Earth observation (EO); journalism 3.0; media industry; journalistic workflow; journalistic practices; text generation with artificial intelligence (AI); disaster reporting; EarthPress
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MDPI and ACS Style

Tsourma, M.; Zamichos, A.; Efthymiadis, E.; Drosou, A.; Tzovaras, D. An AI-Enabled Framework for Real-Time Generation of News Articles Based on Big EO Data for Disaster Reporting. Future Internet 2021, 13, 161. https://doi.org/10.3390/fi13060161

AMA Style

Tsourma M, Zamichos A, Efthymiadis E, Drosou A, Tzovaras D. An AI-Enabled Framework for Real-Time Generation of News Articles Based on Big EO Data for Disaster Reporting. Future Internet. 2021; 13(6):161. https://doi.org/10.3390/fi13060161

Chicago/Turabian Style

Tsourma, Maria, Alexandros Zamichos, Efthymios Efthymiadis, Anastasios Drosou, and Dimitrios Tzovaras. 2021. "An AI-Enabled Framework for Real-Time Generation of News Articles Based on Big EO Data for Disaster Reporting" Future Internet 13, no. 6: 161. https://doi.org/10.3390/fi13060161

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