Evaluating Training Parameter Impacts on TransU-Net Performance for UAV-Based Landslide Prediction

Landslides are among the most destructive geological hazards in Malaysia, especially in mountainous and forested areas. Unmanned aerial vehicle (UAV) imagery offers high spatial resolution and flexible data capture, but deep learning performance is highly sensitive to training hyperparameters. In this study, the TransU-Net model for UAV-based landslide detection was adopted and a systematic ablation study on learning-rate and epoch settings using a coarse-to-fine tuning strategy. The Berembun Forest Reserve dataset was first used to determine the optimal training configuration. Then, the optimised configuration was tested on multiple UAV sub-datasets in the CAS Landslide dataset to evaluate performance stability under different terrain properties and spatial resolutions. The optimised configuration yielded the best F1-score (0.9598) and IoU of 0.9507 on the Berembun Forest Reserve dataset, and consistently high F1-scores across the evaluated CAS Landslide sub-datasets. Qualitative visualisation analysis also revealed good spatial correspondence between the predicted segmentation masks and the ground-truth annotations. Variations in Intersection over Union (IoU) values were mainly associated with boundary delineation uncertainty rather than severe misclassification. Overall, the results show that the performance of UAV-based landslide segmentation can improve by systematic hyperparameter tuning, and the optimised TransU-Net configuration under the evaluated terrain conditions yields promising results.