Fig. : Pipeline for developing an expert-augmented deep learning system that generates interpretable route assessments through three stages: training a data-driven model to predict route score, establishing an augmented scoring framework embedding expert knowledge based on expert analysis of model predictions, and defining assessment criteria to translate scores into clear qualitative evaluations. ranking. Specifically, the prediction of the model underwent expert analysis to establish a robust scoring framework that accounts for critical factors identified by domain experts during the analysis. Finally, within the system, we defined interpretable assessment criteria by mapping the numerical scores to meaningful qualitative evaluations, enabling clear communication of route quality and feasibility to end users. This integration of data-driven predictions with expert knowledge creates a more reliable and transparent route assessment system.