# DINOv3-Driven Semantic Segmentation for Landslide Mapping in Mountainous Regions

## TL;DR

This paper introduces a new method for mapping landslides in mountainous areas using advanced AI techniques to handle different types of remote sensing data.

## Contribution

The study proposes a segmentation framework using DINOv3 to extract sensor-robust representations for landslide mapping.

## Key findings

- The framework achieved a Dice coefficient of 0.96 on the Longxi satellite dataset.
- It achieved a Dice coefficient of 0.965 on the Longxi UAV dataset.
- Performance was consistent across different sensor resolutions and appearances.

## Abstract

Landslide hazard assessment increasingly demands the joint analysis of heterogeneous remote sensing data; however, automating this process remains difficult due to the pronounced resolution and texture discrepancies existing between satellite and aerial sensors. To address these limitations, this study proposes a robust segmentation framework capable of extracting sensor-robust representations. The framework leverages a DINOv3 transformer encoder and exploits representations from multiple transformer layers to capture complementary visual information, ranging from fine-grained surface textures to global semantic contexts, overcoming the receptive field constraints of conventional CNNs. Experiments on the Longxi satellite dataset achieve a Dice coefficient of 0.96 and an IoU of 0.938, and experiments on the Longxi UAV dataset achieve a Dice coefficient of 0.965 and an IoU of 0.941. These results show consistent segmentation performance on both the Longxi satellite and UAV datasets, despite differences in spatial resolution and surface appearance between acquisition platforms.