TRB-Net: Terrain-Residual and Boundary-Assisted Multimodal Martian Landslide Segmentation on a Local MMLSv2 Split

Abstract

Martian landslide segmentation is challenging because annotated samples are limited and landslide deposits can have weak boundaries, heterogeneous textures, and visual similarity to crater rims and canyon walls. This study evaluates a terrain-residual and boundary-assisted network (TRB-Net) on the locally available MMLSv2 train/validation/test split. TRB-Net combines RGB texture with digital elevation model (DEM), slope, thermal inertia, and grayscale information through terrain-residual fusion, an atrous spatial pyramid pooling decoder, and auxiliary boundary supervision. The compact evaluation checkpoint, using a validation-selected threshold of 0.55, achieves an mIoU of 0.8060, foreground IoU of 0.7502, F1-score of 0.8573, precision of 0.8473, and recall of 0.8676 with 5.255 million parameters. In same-split comparisons, DeepLabV3+ obtains the highest overlap scores, while TRB-Net provides competitive segmentation and an explicit architecture for tracing how terrain and boundary cues enter the prediction. These results apply only to the local MMLSv2 split; geographically isolated and large-area Martian mapping performance were not evaluated.