EarthLD: Towards Unified Open-World Landslide Understanding via Vision-Language Guided Diffusion Models

2026-09-01

AI Summary

为解决滑坡自动检测与映射难题，提出EarthLD框架，通过视觉-语言引导的扩散模型实现滑坡识别、映射及触发因素解析。

Abstract

Landslides are widespread geological hazards, yet their automated detection and mapping in remote sensing imagery remain challenging because of their irregular morphology, ambiguous spectral signatures, and substantial domain shifts across imaging platforms. To overcome these challenges, we propose EarthLD, a vision-language-guided diffusion framework for open-world landslide understanding, enabling unified landslide recognition, mapping, and trigger interpretation. At its core, EarthLD formulates landslide understanding as a diffusion process that progressively infers the presence, spatial extent, and pixel-level boundaries of landslides from noisy latent representations. This probabilistic formulation enables the model to jointly perform image-level landslide recognition and mapping while characterizing predictive uncertainty. By integrating visual observations with contextual knowledge in the denoising process, EarthLD distinguishes diverse landslides from backgrounds, produces confidence-aware predictions for suspected regions, and maps landslide ranges. We additionally construct a global-scale open-world landslide benchmark by systematically harmonizing multiple publicly available remote sensing data collected by diverse institutions. Extensive experiments across regions, sensors, and triggering events demonstrate that EarthLD consistently outperforms existing landslide detection methods, highlighting its potential as a unified and robust solution for global geological-hazard monitoring and emergency response.

Problem

• Landslides
• Remote Sensing Imagery
• Automated Detection
• Mapping
• Domain Shifts

Innovation

• vision-language guided diffusion
• open-world landslide understanding
• predictive uncertainty
• confidence-aware predictions