Deep Learning-based Study on Assessment and Enhancement Strategy for Geological Disaster Emergency Evacuation Capacity in Changbai Mountain North Scenic Area

Abstract

This research focuses on enhancing emergency evacuation capabilities under geological disasters in the northern scenic area of Changbai Mountain. The research leverages advanced technology, employing the Real-Enhanced Super-Resolution Generative Adversarial Network (Real-ESRGAN), significantly enhancing image clarity. It is then combined with deep learning methods for a comprehensive assessment of emergency evacuation capacity, facilitating a more accurate understanding of the current scenic area situation. Introducing a 3D model based on elevation, slope, and slope direction enhances the geographic overview, offering a 360° view for a more detailed understanding of terrain and geomorphological features. Considering geographic and climatic factors, the study proposes targeted improvements tailored to the specific characteristics of the scenic area. The innovation of this study lies in its successful resolution of remote sensing image blurring using Real-ESRGAN, introducing an advanced method for small-area studies. Through these integrated tools and methods, the study significantly enhances the accuracy of data processing, assessment, and decision support. It demonstrates an integrated approach to scenic area research, contributing to geohazard management, emergency planning, and the overall safety of the Changbai Mountain scenic area.