LARGE-SCALE MULTIMODAL TRANSFORMERS FOR INTEGRATING SATELLITE IMAGERY, SEISMIC DATA, AND CLIMATE VARIABLES: UNIFIED AI FRAMEWORK FOR SPATIOTEMPORAL REASONING AND PREDICTIVE EARTH SYSTEM MODELING IN HIGH-UNCERTAINTY REGIONS

The increasing unpredictability of climate events, coupled with fragmented observational data sources, has necessitated advanced frameworks for unified earth system modeling. This paper proposes a Large-Scale Multimodal Transformer (LSMT) framework integrating satellite imagery, seismic data, and climate variables to enhance spatiotemporal reasoning and predictive accuracy in high-uncertainty regions. By utilizing multimodal deep learning and attention-based architectures, the system can dynamically fuse heterogeneous data, yielding more resilient earth system forecasts. The framework leverages a combination of pretrained transformer encoders fine-tuned on domain-specific datasets, a unified alignment module, and a spatiotemporal decoder to perform predictive modeling. Evaluation across multiple scenarios, including seismic hotspot prediction and extreme climate event forecasting, demonstrates superior performance over single-modality baselines. This study contributes a scalable and generalizable method for environmental intelligence, capable of supporting early-warning systems and adaptive planning in geospatially vulnerable ecosystems.