GeoAI: spatially explicit artificial intelligence techniques for geographic knowledge discovery and beyond

Funding

Authorship of this paper includes an employee of UT-Battelle, LLC, under contract DE-AC05- 00OR22725 with the U.S. Department of Energy. Accordingly, the United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. Krzysztof Janowicz acknowledges support by the National Science Foundation under award 1936677 ‘Convergence Accelerator Phase I (RAISE): Spatially-explicit Models, Methods, and Services for Open Knowledge Networks’ . Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation. Grant McKenzie acknowledges support from the Natural Sciences and Engineering Research Council of Canada. Song Gao acknowledges support from the National Science Foundation (Grant No. 1940091) and the Wisconsin Alumni Research Foundation. Finally, the authors would like to thank Gengchen Mai and Bo Yan for sharing comments and insights.