Abstract
The Annual Meeting of the American Association of Geographers (AAG) in 2023 marked a five-year milestone since the first Geospatial Artificial Intelligence (GeoAI) Symposium was held at AAG in 2018. In the past five years, progress has been made while open questions remain. In this context, we organized an AAG panel and invited five panellists to discuss the advances and limitations in GeoAI research. The panellists commended the successes, such as the development of spatially explicit models, the production of large-scale geographic datasets, and the use of GeoAI to address real-world problems. The panellists also shared their thoughts on limitations in current GeoAI research, which were considered as opportunities to engage theories in geography, enhance model explainability, quantify uncertainty, and improve model generalizability. This article summarizes the presentations from the panellists and also provides after-panel thoughts from the organizers. We hope that this article can make these thoughts more accessible to interested readers and help stimulate new ideas for future breakthroughs.
| Original language | English |
|---|---|
| Pages (from-to) | 1-14 |
| Number of pages | 14 |
| Journal | Annals of GIS |
| Volume | 30 |
| Issue number | 1 |
| DOIs | |
| State | Published - 2024 |
Funding
Yingjie Hu acknowledges support from the U.S. National Science Foundation (NSF) under Grant No. BCS-2117771. May Yuan’s effort is based upon work supported by (while serving at) the NSF. Wenwen Li acknowledges support from the NSF under Grant No. BCS-1853864 and OAC-2230034. Song Gao acknowledges support from the NSF under Grant No. OAC-2112606. The statements included in the article are solely the authors’ and do not necessarily reflect the view of the NSF. This manuscript has been co-authored by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan ( https://energy.gov/downloads/doe-public-access-plan ).
| Funders | Funder number |
|---|---|
| National Science Foundation | BCS-1853864, BCS-2117771, OAC-2112606, OAC-2230034 |
| U.S. Department of Energy |
Keywords
- GeoAI
- Giscience
- geography
- perspectives
- theory