A Causally Informed Pretraining Approach for Multimodal Foundation Models: Applications in Remote Sensing

Abstract

Self-supervised learning has emerged as a powerful paradigm for pretraining foundation models using large-scale data. Existing pretraining approaches predominantly rely on masked reconstruction or next-token prediction strategies, demonstrating strong performance across various downstream tasks, including geoscience applications. However, these approaches do not fully capture the causal interplay between different geospatial and environmental variables. To address this limitation, we propose Causally Informed Variable-Step Forecasting (CI-VSF), a novel pretraining task that models forecasting as a conditional generation task, where driver variables (e.g., weather) inform the prediction of response variables (e.g., satellite imagery). We demonstrate that pretraining in such a fashion leads to enhanced performance when finetuned on both prediction (e.g., crop mapping, missing image prediction, soil moisture estimation) and forecasting (e.g., future image forecasting, soil moisture forecasting) downstream tasks when compared to other pretraining approaches. While we use remote sensing as our main application to demonstrate the efficacy of our proposed pretraining strategy over existing paradigms, it is applicable to any domain that involves known causal relationships amongst a set of variables.