KI - Künstliche Intelligenz

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The Scientific journal "KI – Künstliche Intelligenz" is the official journal of the division for artificial intelligence within the "Gesellschaft für Informatik e.V." (GI) – the German Informatics Society – with contributions from throughout the field of artificial intelligence. The journal presents all relevant aspects of artificial intelligence – the fundamentals and tools, their use and adaptation for scientific purposes, and applications which are implemented using AI methods – and thus provides the reader with the latest developments in and well-founded background information on all relevant aspects of artificial intelligence. For all members of the AI community the journal provides quick access to current topics in the field and promotes vital interdisciplinary interchange.

Authors with most Documents  

Visser, Ubbo
21
Ragni, Marco
13
Richter, Kai-Florian
12
Sonntag, Daniel
12
Beetz, Michael
11

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2017 - 201912020 - 202411

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Author: search.filters.author.Richter, Kai-Florian

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1 - 10 of 12
  • Journal Article
    Eye of the Beholder
    (Springer, 2024) Richter, Kai-Florian
  • Journal Article
    Pragmatic GeoAI: Geographic Information as Externalized Practice
    (Springer, 2023) Scheider, Simon; Richter, Kai-Florian
    Current 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.
  • Journal Article
    GeoAI
    (Springer, 2023) Scheider, Simon; Richter, Kai-Florian
  • Journal Article
    Current topics and challenges in geoAI
    (Springer, 2023) Richter, Kai-Florian; Scheider, Simon
    Taken literally, geoAI is the use of Artificial Intelligence methods and techniques in solving geo-spatial problems. Similar to AI more generally, geoAI has seen an influx of new (big) data sources and advanced machine learning techniques, but also a shift in the kind of problems under investigation. In this article, we highlight some of these changes and identify current topics and challenges in geoAI.
  • Journal Article
    GeoAI and Beyond
    (Springer, 2023) Scheider, Simon; Richter, Kai-Florian; Janowicz, Krzysztof
  • Journal Article
    GeoAI as Collaborative Effort
    (Springer, 2023) Richter, Kai-Florian; Scheider, Simon; Tuia, Devis
  • Journal Article
    GeoAI as Collaborative Effort
    (Springer, 2023) Richter, Kai-Florian; Scheider, Simon; Tuia, Devis
  • Journal Article
    GeoAI
    (Springer, 2023) Scheider, Simon; Richter, Kai-Florian
  • Journal Article
    GeoAI and Beyond
    (Springer, 2023) Scheider, Simon; Richter, Kai-Florian; Janowicz, Krzysztof
  • Journal Article
    Pragmatic GeoAI: Geographic Information as Externalized Practice
    (Springer, 2023) Scheider, Simon; Richter, Kai-Florian
    Current 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.