Encoder Choice Outweighs Modular Refinement in U-Net Architectures for Globally Distributed Coseismic Landslide Segmentation

Rapid post-earthquake landslide mapping is critical for emergency response, yet the quantitative hierarchy of architectural choices in U-Net-based segmentation remains unresolved. Using the Globally Distributed Coseismic Landslide Dataset (GDCLD; nine earthquakes, four continents), we conducted controlled ablations to isolate the contributions of encoder backbones (EfficientNet-b4, ResNet34, DenseNet121) and task-specific modules (RFB, CBAM, PANet). EfficientNet-b4 outperformed ResNet34 by 11.02 pp IoU; per-image IoU differed significantly across the three evaluated encoders (two-sided Kruskal–Wallis test, p < 0.0001), exceeding the cumulative gain of all downstream modules (RFB +0.34 pp, CBAM +0.54 pp, PANet +0.75 pp). EfficientNet regression metrics (MAE, MSE, R2) showed no statistically significant differences across module ablations (p > 0.26), suggesting that gains arose primarily from decision-boundary sharpening rather than large probability-map changes. The final model achieved 82.79% Dice, 71.15% IoU, and 86.86% Recall, with consistent performance across diverse geomorphological and imaging conditions within the present GDCLD evaluation. Independent event-held-out and external-dataset tests are still required to establish broader geographic generalization. These results demonstrate performance within the present GDCLD evaluation; independent event-held-out testing remains necessary for definitive cross-region validation. These findings establish a two-tier design hierarchy—encoder backbones provide the dominant performance capacity within the evaluated U-Net framework, while modules refine decisions via a “see–purify–delineate” mechanism—and propose a dual-metric diagnostic protocol for future architectural evaluation. This hierarchy is limited to the architectures and experimental settings evaluated in this study.