Deep learning-based study on assessment and enhancement strategy for geological disaster emergency evacuation capacity in Changbai Mountain North Scenic Area.

This study focuses on the northern scenic area of Changbai Mountain, aiming to evaluate the emergency evacuation capacity of the region in the context of geological disasters and to formulate corresponding improvement strategies. Due to the relatively small area of this region, difficulties in data acquisition, and insufficient precision, traditional models for evaluating emergency evacuation capacity are typically applied to urban built environments, with relatively few studies addressing scenic areas. To tackle these issues, this research employs the Real-Enhanced Super-Resolution Generative Adversarial Network (Real-ESRGAN), which successfully resolves the problem of blurriness in remote sensing images and significantly enhances image clarity. Coupled with the Graph Convolutional Network (GCN) model, the study calculates the emergency evacuation time for each raster point, providing a comprehensive assessment of the region's evacuation capacity. Based on the evaluation results, the study proposes targeted improvement measures for areas identified as having poor emergency evacuation capacity, taking into account the existing infrastructure of the scenic area. By constructing an indicator system encompassing effectiveness, accessibility, and safety, the feasibility of each proposed enhancement strategy is assessed scientifically and rationally. Through these integrated tools and methodologies, this research significantly improves the accuracy of data processing, evaluation, and decision support, showcasing a comprehensive approach to scenic area research that provides critical support for geological disaster management, emergency planning, and the overall safety of the Changbai Mountain scenic area.