Artificial Intelligence Approaches
Yingjie Hu, Wenwen Li, Dawn Wright, Orhun Aydin, Daniel Wilson, Omar, Maher, Mansour Raad
TL;DR
This paper reviews recent advances in artificial intelligence, especially machine learning and deep learning, highlighting their integration with geography to address environmental and societal challenges, and discusses future directions in GeoAI.
Contribution
It provides a comprehensive overview of recent developments in AI and its integration with geographic information science, outlining applications and future research directions.
Findings
AI, especially machine learning and deep learning, has advanced rapidly.
GeoAI enables novel solutions for environmental and societal problems.
Future directions include expanding GeoAI applications and addressing current limitations.
Abstract
Artificial Intelligence (AI) has received tremendous attention from academia, industry, and the general public in recent years. The integration of geography and AI, or GeoAI, provides novel approaches for addressing a variety of problems in the natural environment and our human society. This entry briefly reviews the recent development of AI with a focus on machine learning and deep learning approaches. We discuss the integration of AI with geography and particularly geographic information science, and present a number of GeoAI applications and possible future directions.
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