A comprehensive GeoAI review: Progress, Challenges and Outlooks

TL;DR

This comprehensive review explores the progress, challenges, and future outlooks of GeoAI, highlighting its applications, methodological approaches, and the integration with GIS and big geodata to advance geospatial intelligence.

Contribution

It provides an extensive overview of GeoAI, including methodologies, applications, challenges, and future directions, serving as a valuable resource for researchers and practitioners.

Findings

GeoAI is increasingly used in fields like agriculture, environmental monitoring, and urban planning.

Significant challenges include data quality, model interpretability, and computational complexity.

Future prospects involve integrating GeoAI with emerging technologies and addressing current limitations.

Abstract

In recent years, Geospatial Artificial Intelligence (GeoAI) has gained traction in the most relevant research works and industrial applications, while also becoming involved in various fields of use. This paper offers a comprehensive review of GeoAI as a synergistic concept applying Artificial Intelligence (AI) methods and models to geospatial data. A preliminary study is carried out, identifying the methodology of the work, the research motivations, the issues and the directions to be tracked, followed by exploring how GeoAI can be used in various interesting fields of application, such as precision agriculture, environmental monitoring, disaster management and urban planning. Next, a statistical and semantic analysis is carried out, followed by a clear and precise presentation of the challenges facing GeoAI. Then, a concrete exploration of the future prospects is provided, based on several informations gathered during the census. To sum up, this paper provides a complete overview of the correlation between AI and the geospatial domain, while mentioning the researches conducted in this context, and emphasizing the close relationship linking GeoAI with other advanced concepts such as geographic information systems (GIS) and large-scale geospatial data, known as big geodata. This will enable researchers and scientific community to assess the state of progress in this promising field, and will help other interested parties to gain a better understanding of the issues involved.