Auflistung nach Autor:in "Richter, Kai-Florian"
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- ZeitschriftenartikelCurrent topics and challenges in geoAI(KI - Künstliche Intelligenz: Vol. 37, No. 1, 2023) Richter, Kai-Florian; Scheider, SimonTaken 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.
- ZeitschriftenartikelGeoAI(KI - Künstliche Intelligenz: Vol. 37, No. 1, 2023) Scheider, Simon; Richter, Kai-Florian
- ZeitschriftenartikelGeoAI and Beyond(KI - Künstliche Intelligenz: Vol. 37, No. 1, 2023) Scheider, Simon; Richter, Kai-Florian; Janowicz, Krzysztof
- ZeitschriftenartikelGeoAI as Collaborative Effort(KI - Künstliche Intelligenz: Vol. 37, No. 1, 2023) Richter, Kai-Florian; Scheider, Simon; Tuia, Devis
- ZeitschriftenartikelIdentifying Landmark Candidates Beyond Toy Examples(KI - Künstliche Intelligenz: Vol. 31, No. 2, 2017) Richter, Kai-FlorianIncorporating references to landmarks in navigation systems requires having data on potential landmarks in the first place. While there have been many approaches in the scientific literature for identifying landmark candidates, these have hardly been picked up in actual, running systems. One major obstacle for this to happen may be that most—if not all—approaches presented so far are not scalable due to their underlying data requirements. In this paper, I will critically discuss existing approaches in light of their scalability. I will then suggest a way forward to more scalable solutions by combining in a smart way aspects of different approaches.
- ZeitschriftenartikelPragmatic GeoAI: Geographic Information as Externalized Practice(KI - Künstliche Intelligenz: Vol. 37, No. 1, 2023) Scheider, Simon; Richter, Kai-FlorianCurrent 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.