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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 <jats:italic>structuralist<\/jats:italic> 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 <jats:italic>pragmatic turn<\/jats:italic> in GeoAI is required to overcome this problem.<\/jats:p>","DOI":"10.1007\/s13218-022-00794-2","type":"journal-article","created":{"date-parts":[[2023,1,20]],"date-time":"2023-01-20T15:03:34Z","timestamp":1674227014000},"page":"17-31","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Pragmatic GeoAI: Geographic Information as Externalized Practice"],"prefix":"10.1007","volume":"37","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2267-4810","authenticated-orcid":false,"given":"Simon","family":"Scheider","sequence":"first","affiliation":[]},{"given":"Kai-Florian","family":"Richter","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,1,20]]},"reference":[{"key":"794_CR1","doi-asserted-by":"publisher","DOI":"10.1111\/gean.12312","author":"K Akbari","year":"2021","unstructured":"Akbari K, Winter S, Tomko M (2021) Spatial causality: a systematic review on spatial causal inference. 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