“Geospatial artificial intelligence (geoAI) is an emerging science that utilizes advances in high-performance computing to apply technologies in AI, particularly machine learning (e.g., deep learning) and data mining to extract meaningful information from spatial big data.” (VoPham et al. 2018)

“GeoAI […] refers to integrating state-of-the-art deep learning-based AI techniques with GIScience, aligned with human values, for geographical applications and geospatial knowledge discovery.” (Liu 2019)

“GeoAI as a subfield of spatial data science utilizes advancements in techniques and data cultures to support the creation of more intelligent geographic information as well as methods, systems, and services for a variety of downstream tasks.” (Janowicz et al. 2020)

“GeoAI, or geospatial artificial intelligence, sits at the junction of AI, geospatial big data, and high performance computing […] to provide a promising solution technology for data- or compute-intensive geospatial problems.” (Li 2020)

“GeoAI can be regarded as a study subject to develop intelligent computer programs to mimic the processes of human perception, spatial reasoning, and discovery about geographical phenomena and dynamics; to advance our knowledge; and to solve problems in human environmental systems and their interactions, with a focus on spatial contexts and roots in geography or geographic information science […].” (Gao 2021)

“GeoAI is defined broadly as the application of artificial intelligence (AI) methods and techniques to geospatial data, processes, models, and applications. GeoAI includes AI methods, geospatial big data, and high-performance computing […] to provide technology solutions for data and computationally intensive geospatial problems.” (Usery et al. 2022)

“GeoAI consists of all geographic information practices that make use of maps to satisfy some information purpose, but which a computer cannot (yet) master.” (Scheider & Richter 2023)

References