Speaker
Description
For decades, geographers have acknowledged the potential of artificial intelligence (AI) to inform geographical problem solving (Couclelis, 1986; Openshaw & Openshaw, 1997; Smith, 1984). More recent applications have demonstrated the ability of AI to assist in analyzing remote sensing, GIS, and other geographic data (Hu, 2018; Janowicz, et al., 2019). The advent of generative AI feels different, however, as programs like ChatGPT, Bard, DALL-E, and others have increased the accessibility of AI and have broadened the application thereof to all aspects of the geography workplace. Therefore, discussion is needed to develop expectations, guidelines, and best practices for the use of generative AI by applied geographers. Alignment of best practices in academia, government, and industry is necessary for proper training, expectations, and the health of the discipline. Come share your experiences and thoughts and help applied geographers be at the forefront of this global issue.
References:
Couclelis, H. (1986). Artificial intelligence in geography: Conjectures on the shape of things to come. The Professional Geographer, 38(1), 1-11.
Hu, Y. (2018). Geo‐text data and data‐driven geospatial semantics. Geography Compass, 12(11), 10.1111/gec3.12404
Janowicz, K., Gao, S., McKenzie, G., Hu, Y., & Bhaduri, B. (2020). GeoAI: spatially explicit artificial intelligence techniques for geographic knowledge discovery and beyond. International Journal of Geographical Information Science, 34(4), 625-636.
Openshaw, S., & Openshaw, C. (1997). Artificial Intelligence in Geography. John Wiley & Sons, Inc.
Smith, T. R. (1984). Artificial intelligence and its applicability to geographical problem solving. The Professional Geographer, 36(2), 147-158.
