Land Deformation Prediction via Multi-modal Adaptive Association Learning

Accurate land deformation prediction using InSAR (Interferometric Synthetic Aperture Radar) technology is crucial for early warning of geological disasters. However, existing prediction methods face two major challenges: cross-area association bottleneck and inadequate handling of temporal distribution heterogeneity. To address these challenges, we propose Multi-modal Adaptive Association Learning framework (MAAL). For the spatial knowledge transfer challenge, we introduce a cross-area multi-modal association learning module that integrates multi-modal (InSAR and geological text) data to enable knowledge transfer between areas with similar geological characteristics. For temporal distribution heterogeneity, we develop an adaptive evolution stage recognition module that uses distribution routers to identify different temporal patterns, then applies corresponding linear extractors to model the heterogeneous landslide evolution. Experimental validation on 889 hazardous areas demonstrates that MAAL outperforms baselines.