Selecting the content of textual descriptions of geographically located events in spatio-temporal weather data

Ross Turner, Gowri Somayajulu Sripada, Ehud Baruch Reiter, Ian P. Davy

Research output: Chapter in Book/Report/Conference proceedingConference contribution


In several domains spatio-temporal data consisting of references to both space and time are collected in large volumes. Textual summaries of spatio-temporal data will complement the map displays used in Geographical Information Systems (CIS) to present data to decision makers. In the RoadSafe project we are working on developing Natural Language Generation (NLG) techniques to generate textual summaries of spatio-temporal numerical weather prediction data. Our approach exploits existing video processing techniques to analyse spatio-temporal weather prediction data and uses Qualitative Spatial Reasoning(QSR) techniques to reason with geographical data in order to compute the required content (information) for generating descriptions of geographically located events. Our evaluation shows that our approach extracts information similar to human experts.

Original languageEnglish
Title of host publicationApplications and Innovations in Intelligent Systems XV
Subtitle of host publicationProceedings of AI-2007, the Twenty-seventh SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence
EditorsRichard Ellis, Tony Allen, Miltos Petridis
Place of PublicationGodalming
Number of pages14
ISBN (Print)978-1-84800-085-8
Publication statusPublished - 2008

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