A Review of the Integration Between Geospatial Artificial Intelligence and Remote Sensing

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 geographic information. Nevertheless, there
remains a shortage of comprehensive research on the specific applications of AI in geospatial contexts.
Consequently, this study aims to 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 delves into the innovations and tools employed for data acquisition in geomatics, focusing on RGB
images, thermal images, 3D point clouds, GPS coordinates, and hyperspectral/multispectral images. Subsequently,
we elucidate how AI techniques have successfully extracted valuable insights from geomatics data. Furthermore,
we present various practical scenarios where AI has been deployed and the specific methodologies employed for
each case. Through this exploration, we aim to highlight the immense potential of AI in geomatics and stimulate
future research endeavors.