Work

Landslideagent with multimodal landslidebench: a domain-rule-augmented agent for autonomous landslide identification and analysis

Year: 2026

Type: article

Abstract: Intelligent identification and analysis of landslides are critical for disaster prevention. However, current interpretation paradigms struggle to simultaneously extract visual features and geoscientific semantics, while general-purpose vision–language models (VLMs) suffer from perceptual limitations and domain hallucinations in complex geological scenarios. This paper proposes an instruction-driven agentic framework for landslide response that integrates a multimodal benchmark, a specialized VLM, and a rule-enhanced agent. First, a multimodal fine-grained landslide dataset LandslideBench with seven landslide subtype labels, high-resolution imagery, pixel-level masks, and high-quality textual descriptions is constructed through multi-VLM cross-validation and interactive annotation. Second, a landslide-oriented dual-head vision-language method, LandslideVLM, is proposed and fine-tuned on LandslideBench via low-rank adaptation, in which a discriminative classification head and a generative language head are integrated to jointly support fine-grained landslide classification and geoscientific semantic description. Finally, a domain-rule-enhanced LandslideAgent is developed by integrating structured report metadata constraints and cross-validation identification constraints. Experimental results demonstrate that LandslideBench can serve as an effective benchmark for landslide interpretation. The fine-tuned LandslideVLM achieves accuracy improvements of 12.79%, 55.71%, and 17.27% on landslide discrimination, fine-grained classification, and semantic description quality, respectively. Across 28 paired cases, rule-constrained LandslideAgent achieves an accuracy of 88.7% across six evidence-checkable fields, outperforming free-mode agent by 11.9%.

Source: International Journal of Digital Earth

DOI: 10.1080/17538947.2026.2755143