Work

GeoAI for Science and the Science of GeoAI

Year: 2024

Type: article

Abstract: This paper reviews trends in GeoAI research and discusses cutting-edge advances in GeoAI and its roles in accelerating environmental and social sciences. It addresses ongoing attempts to improve the predictability of GeoAI models and recent research aimed at increasing model explainability and reproducibility to ensure trustworthy geospatial findings. The paper also provides reflections on the importance of defining the "science" of GeoAI in terms of its fundamental principles, theories, and methods to ensure scientific rigor, social responsibility, and lasting impacts.

Source: Journal of Spatial Information Science

DOI: 10.5311/josis.2024.29.349

Authors: Wenwen Li, Samantha Arundel, Song Gao, Michael Goodchild, Yingjie Hu, Shaowen Wang, Alexander Zipf

Institutions: Arizona State University, United States Geological Survey, University of Wisconsin–Madison, University of California, Santa Barbara, University at Buffalo, State University of New York, University of Illinois Urbana-Champaign, Heidelberg University

Language: English

Topic: Artificial Intelligence Applications

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: diamond

Funder: National Science Foundation

Awards: MCA: Career Advancement in Polar Cyberinfrastructure: Permafrost Feature Mapping and Change Detection using Geospatial Artificial Intelligence and Remote Sensing, AI Institute for Intelligent CyberInfrastructure with Computational Learning in the Environment (ICICLE), HDR Institute: Geospatial Understanding through an Integrative Discovery Environment, Collaborative Research: CyberTraining: Implementation: Medium: Cyber2A: CyberTraining on AI-driven Analytics for Next Generation Arctic Scientists, GeoAI for Terrain Analysis: A Deep-Learning Approach for Landform Feature Detection

OpenAlex ID: W4402695703

FWCI: 6.043

Cites:

Cited by: 41

Related to: 10