When Machine Learning Meets Geospatial Data: A Comprehensive GeoAI Review - Anasse Boutayeb; Iyad Lahsen-Cherif; Ahmed El Khadimi (IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing)
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 article offers a comprehensive review of GeoAI as a synergistic concept applying artificial intelligence (AI) models, specifically those of machine learning (ML), 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 information gathered during the census. To sum up, this article provides a complete overview of the correlation between ML and the geospatial domain, while mentioning the research conducted in this context, and emphasizing the close relationship linking GeoAI with other advanced concepts such as geographic information systems 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.