Remote sensing change detection
Remote sensing change detection aims to identify meaningful changes between images of the same geographical area acquired at different times. It supports applications such as environmental monitoring, land-use analysis, disaster assessment, and infrastructure planning.
Our research examines both supervised and unsupervised approaches. We study how fundamental choices, including pretrained representations, model architecture, training strategy, and evaluation protocol, affect performance and generalization. We are also developing methods that learn without manually annotated change masks by generating diverse, data-driven perturbations in latent feature spaces. This makes it possible to address rare or complex changes and extend the methods to different sensing modalities, including optical and synthetic-aperture radar imagery.