Geospatial Artificial Intelligence (GeoAI)

Abstract

Nowadays, artificial intelligence (AI) is bringing tremendous new opportunities and challenges to geospatial research. Its fast development is powered by theoretical advancement, big data, computer hardware (e.g., the graphics processing unit, or GPU), and high-performance computing platforms that support the development, training, and deployment of AI models within a reasonable amount of time. Recent years have witnessed significant advances in geospatial artificial intelligence (GeoAI), which is the integration of geospatial studies and AI, especially machine learning and deep learning methods and the latest AI technologies in both academia and industry. GeoAI can be regarded as a study subject to develop intelligent computer programs to mimic the processes of human perception, spatial reasoning, and discovery about geographical phenomena and dynamics; to advance our knowledge; and to solve problems in human environmental systems and their interactions, with a focus on spatial contexts and roots in geography or geographic information science (GIScience). Thus, it would require the knowledge of AI theory, programming and computation practices as well as geographic domain knowledge to be competent in GeoAI research. There have already been increasingly collaborative GeoAI studies for GIScience, remote sensing, physical environment, and human society. It is a good time to provide a key reference list for educators, students, researchers, and practitioners to keep up with the latest GeoAI research topics. This bibliographical entry will first review the historical roots for AI in geography and GIScience and then list up to ten selective recent works with annotations that briefly describe their importance for each topic of interest in the GeoAI landscape, ranging from fundamental spatial representation learning to spatial predictions and to various advancements in cartography, earth observation, social sensing, and geospatial semantics.