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Abstract

Experts have extensively explored the advantages and applications of modern artificial intelligence (AI) algorithms across various domains. Geomatics data processing is no exception, as AI offers significant opportunities in this field. However, understanding how AI can be customized or developed to meet the unique requirements of geomatics data is crucial. Integrating AI techniques into geomatics has given rise to Geospatial Artificial Intelligence (GeoAI), a novel approach to uncovering geographicinformation. Nevertheless, there remains a shortage of comprehensive research on the specific applications of AI in geospatial contexts. Consequently, this study aimsto establish AI-based methodologies for the analysis and interpretation of complex geomatics data, bridging existing gaps, and elucidating the connections between AI principles and geomatics data. This paper delvesinto the innovations and tools employed for data acquisition in geomatics, focusingon RGB images, thermal images, 3D point clouds, GPS coordinates, and hyperspectral/multispectral images. Subsequently, we elucidate how AI techniques have successfully extractedvaluable insights from geomatics data. Furthermore, we present variouspractical scenarios where AI has been deployed andthe specific methodologies employed for each case. Through this exploration, weaim to highlight the immense potential of AI in geomatics and stimulate future research endeavors.

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