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Pragmatic GeoAI: Geographic Information as Externalized Practice

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dc.contributor.authorScheider, Simon
dc.contributor.authorRichter, Kai-Florian
dc.date2023-03-01
dc.date.accessioned2023-08-11T12:28:53Z
dc.date.available2023-08-11T12:28:53Z
dc.date.issued2023
dc.description.abstractCurrent artificial intelligence (AI) approaches to handle geographic information (GI) reveal a fatal blindness for the information practices of exactly those sciences whose methodological agendas are taken over with earth-shattering speed. At the same time, there is an apparent inability to remove the human from the loop, despite repeated efforts. Even though there is no question that deep learning has a large potential, for example, for automating classification methods in remote sensing or geocoding of text, current approaches to GeoAI frequently fail to deal with the pragmatic basis of spatial information, including the various practices of data generation, conceptualization and use according to some purpose. We argue that this failure is a direct consequence of a predominance of structuralist ideas about information. Structuralism is inherently blind for purposes of any spatial representation, and therefore fails to account for the intelligence required to deal with geographic information. A pragmatic turn in GeoAI is required to overcome this problem.de
dc.identifier.doi10.1007/s13218-022-00794-2
dc.identifier.issn1610-1987
dc.identifier.urihttp://dx.doi.org/10.1007/s13218-022-00794-2
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/41900
dc.publisherSpringer
dc.relation.ispartofKI - Künstliche Intelligenz: Vol. 37, No. 1
dc.relation.ispartofseriesKI - Künstliche Intelligenz
dc.subjectAI for geographic information||Explainable AI||Practice of geographic information||Purpose
dc.titlePragmatic GeoAI: Geographic Information as Externalized Practicede
dc.typeText/Journal Article
mci.reference.pages17-31

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Künstliche Intelligenz 37(1) - März 2023