Automated Landform Classification from InSAR-Derived DEMs Using an Enhanced Random Forest Model for Urban Transportation Corridor Hazard Assessment

Interferometric Synthetic Aperture Radar (InSAR)-derived Digital Elevation Models (DEMs) provide critical landform data for monitoring the stability of urban infrastructure, especially for linear infrastructure such as roads and transportation corridors. Traditional landform classification methods are often hindered by incomplete results and require significant manual intervention. To address these challenges, we propose an automated landform classification method based on an enhanced Random Forest (RF) model that integrates Optimization of Decreasing Reduction (ODR) for majority class undersampling and Support Vector Machine Synthetic Minority Oversampling Technique (SVM-SMOTE) for minority class oversampling, specifically to address class imbalance. The method was validated using a dataset of 82,450 expert-labeled samples from approximately 100 km of highway corridors, with independent test sets and ten-fold cross-validation. The enhanced RF model achieved a classification completeness rate of 100% and a macro F-score of 97.0%, significantly outperforming traditional rule-based and standard RF methods. This approach provides robust post-processing support for InSAR-based urban infrastructure monitoring and environmental modeling.