EarthLD：基于视觉-语言引导扩散模型的统一开放世界滑坡理解方法

中文摘要 AI 辅助

滑坡是广泛分布的地质灾害，但在遥感影像中实现自动化检测与制图仍面临挑战，原因在于滑坡形态不规则、光谱特征模糊，且不同成像平台间存在显著域偏移。为克服这些挑战，本文提出EarthLD，一种用于开放世界滑坡理解的视觉-语言引导扩散框架，可实现统一的滑坡识别、制图与触发因素解释。EarthLD的核心是将滑坡理解建模为扩散过程，从噪声隐表示逐步推断滑坡的存在、空间范围及像素级边界。该概率建模使模型能同时执行图像级滑坡识别与制图，同时刻画预测不确定性。通过在去噪过程中整合视觉观测与上下文知识，EarthLD可区分不同滑坡与背景，对疑似区域生成带置信度的预测，并绘制滑坡范围。此外，本文通过系统协调多家机构收集的多种公开遥感数据，构建了全球规模的开放世界滑坡基准。跨区域、传感器及触发事件的大量实验表明，EarthLD始终优于现有滑坡检测方法，凸显其作为全球地质灾害监测与应急响应的统一且鲁棒解决方案的潜力。

英文摘要

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.

发表机构

• University of Macau（澳门大学）
• Aerospace Information Research Institute, Chinese Academy of Sciences（中国科学院空天信息创新研究院）
• Xi’an University of Science and Technology（西安科技大学）

机构由 AI 辅助整理，请以论文原文为准。