Spatiotemporal diffusion model for satellite imagery

Generative AI has demonstrated strong capabilities in learning data distributions and producing realistic outputs. Although traditional approaches like Generative Adversarial Networks (GANs) have largely focused on static images and often fail to model coherent temporal sequences. Diffusion models have recently emerged as a more stable and effective alternative for generating time-sequenced data, yet their application to satellite imagery remains limited. Satellite data poses unique challenges such as multispectral channels and irregular temporal intervals that are poorly addressed by models trained on natural image datasets. To bridge this gap, we propose a spatio-temporal video diffusion model tailored for satellite-based forecasting tasks. Trained on curated datasets from the Landsat and Sentinel missions, our model generates temporally coherent sequences by conditioning on metadata like year, while effectively handling the spectral diversity and uneven sampling intervals characteristic of satellite imagery. When evaluated against retrained state-of-the-art baselines, our method demonstrates superior performance in modelling environmental changes, particularly deforestation, and achieves strong scores on perceptual quality metrics such as Fr´echet Inception Distance (FID) and Learned Perceptual Image Patch Similarity (LPIPS), highlighting its effectiveness for geospatial generative tasks. Our code and data is available at github.com/dfki-av/STDS.