Selecting the Content of Textual Descriptions of Geographically Located Events in Spatio-Temporal Weather Data

Abstract

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 (GIS) 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 spatiotemporal 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.