Self-supervised learning (SSL) represents a shift in machine learning that enables versatile pretrained models to leverage the complex relationships present in dense–oftentimes multispectral and multimodal–remote sensing data. This in turn can accelerate how we address sophisticated downstream geospatial tasks for which current methodologies prove insufficient, ranging from land cover classification to urban building segmentation to crop yield measurement and wildfire forecasting.
This PhD project explores the question of how current SSL methodologies may be altered to tackle remote sensing tasks, and also how to make them amenable to incremental time-series generation as new data regularly comes in from sensing instruments.