SOURCE-LINKED INTELLIGENCE

Geo-analytical transformations for answering questions with data

CORDIS · observation · Publication date unknown

information systems (GIS) and corresponding data sources are of major importance for answering the questions posed by data scientists from various domains of application. The recent trend of adopting Artificial Intelligence (AI)-based methods in Geography and Geoscience has spurred hopes that geographic information can be successfully reused across disciplines without requiring the technical skills of GIS. In this context, geographic question-answering (GeoQA) methods are particularly promising because they enable users without a technical background to answer their questions about geographic space using natural language. However, most geographic questions data scientists may want to answer require some form of geo-analytical transformation of maps. Answer maps need to be (re)generated from data, rather than retrieved from storage. In contrast, stored maps are frequently lacking, outdated, biased, or of insufficient quality. For example, instead of retrieving statistical facts about noise intensity in a city, we might want to know about the coverage of noisy areas in this city. Yet,

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recordType

award

status

SIGNED

region

EU

value

1999500

unit

EUR