The invention relates to the technical field of disaster prevention and reduction, and discloses a mountain area earthquake hidden danger prediction method and
system based on an unmanned aerial vehicle and
deep learning, and the method comprises the steps: carrying out the image collection and
data recording of a to-be-predicted mountain area through employing the unmanned aerial vehicle, obtaining the image data of the to-be-predicted mountain area, and constructing a three-dimensional to-be-predicted
terrain model according to the image data; extracting
terrain information of a to-be-predicted mountainous area from the three-dimensional to-be-predicted
terrain model, collecting geological information of the to-be-predicted mountainous area, and performing
fundamental frequency prediction on the to-be-predicted mountainous area to obtain mountainous area
fundamental frequency information; and performing seismic oscillation amplification effect prediction on the to-be-predicted mountain area according to the mountain area
fundamental frequency information, the geological information and the topographic information, obtaining a seismic oscillation amplification coefficient envelope value of each feature point in the to-be-predicted mountain area under the action of seismic
waves, performing earthquake hidden danger grade classification, and drawing an earthquake hidden danger
distribution diagram of the to-be-predicted mountain area. According to the invention, the problem that the pre-earthquake mountain
earthquake disaster hidden danger is difficult to find is solved, and more effective reference is provided for prevention and treatment of mountain earthquake disasters.