LATIDIA · Investigación

LandslideFM: A Foundation Model Towards Unified Landslide Perception and Understanding

arXiv: 2611.12936v1Tipo de anuncio: nuevo Resumen: La detección inteligente de deslizamientos de tierra es esencial para la prevención de desastres. Sin embargo, los modelos existentes se centran principalmente en tareas visuales aisladas, requieren amplios datos etiquetados y

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arXiv:2611.12936v1 Announce Type: new Abstract: Intelligent landslide detection is essential for disaster prevention. However, existing models primarily focus on isolated visual tasks, require extensive labeled data, and lack deep analytical and scene-understanding capabilities. To address these limitations, we propose LandslideFM, the first unified foundation model that connects visual perception tasks with contextual interpretation of landslides. Specifically, LandslideFM adapts to multiple visual tasks via a Source-Agreement Graduated Trust semi-supervised learning framework that leverages limited labeled data alongside abundant unlabeled data. Furthermore, predicted landslide regions provide explicit spatial guidance to a Vision-Language Model adapted through parameter-efficient fine-tuning, enabling comprehensive landslide reasoning. To support training and evaluation in this domain, we also present