Current application of machine learning models in the analysis of remote sensing survey data for geological hazards

Abstract

To investigate the current landscape of the application of machine learning in remote sensing surveys of geological disasters and to support the development of intelligent remote sensing survey technologies for geological disasters, a bibliometric analysis of machine learning and geological disaster remote sensing survey technology was conducted using the China National Knowledge Infrastructure (CNKI) database. Visual analysis was performed from multiple perspectives, including the number of publications, research hotspots, and research institutions, to describe the research progress of machine learning and geological disaster remote sensing survey technology. VOSviewer software was utilized to scrutinize the high-frequency keywords and their associations between machine learning and geological disaster remote sensing survey technology. The results showed that remote sensing survey technology for geological disasters in China is gradually shifting from traditional “topographic measurement” towards more holistic “topographic and geometric measuremen” approaches. With the advancement of unmanned aerial vehicle remote sensing technology, the new generation of intelligent learning algorithms have emerged as the predominant research direction, fostering the growth of automated geological disaster recognition and intelligent extraction techniques. Nevertheless, the future of remote sensing survey technology for geological disasters is poised to evolve into a comprehensive technical system that emphasizes the synergistic “air-space-ground” application and emergency monitoring. Considering the diverse characteristics of remote sensing image data, the primary developmental trajectory will involve an extensive exploration of various machine learning algorithms across different remote sensing interpretation scenarios.

Keywords

• geologic hazard
• remote sensing
• machine learning
• bibliometrics