GeoAI

Excerpt

Research in artificial intelligence (AI), geography, and geographic information science (GIScience) has had multiple fruitful points of contact during the past decades. More than 30 years ago, [5, 21, 26] suggested how AI methods could be used for spatial modelling and geographic problem-solving, including neural nets for regression modelling, spatial optimisation, spatial pattern recognition, and spatial simulation, but also the use of spatial knowledge bases and expert systems [20]. Thus, from the very beginning of geoAI¹, both data-driven (machine learning (ML), optimisation, and simulation) methods, as well as theory development was taken into focus. While some geographers at the time complained about an apparent lack of theory in AI, [5] argued that a cognitive and computational engineering approach might have the capacity to advance theory as well as method development in geography based on testing formal and computational representations of qualitative as well as quantitative concepts. And indeed, such an approach towards geography and geographic information bore fruits. For example, spatial simulation models formed the basis of urban modelling and complexity science [3], spatial pattern detection, ML classification and regression have been adopted in geographic analysis [14, 17], and natural language processing (NLP) techniques for georeferencing texts [10]. Furthermore, knowledge representation and reasoning methods in AI have inspired the development of spatial calculi for spatial reasoning [4, 6, 30], as well as geospatial knowledge models in the Semantic Web [7], ontologies of space [8], and, more recently, spatial knowledge graphs [11]. These different strands of work have resulted in new geographic information retrieval (GIR) methods [22], digital twins of cities [2], as well as progress in human-computer interaction, orientation, and wayfinding [23]. …