Diffusion Models

Diffusion Models are a class of generative models that learn to create data by reversing a gradual noising process. During training, the model learns to denoise data that has been progressively corrupted with Gaussian noise across many time steps. At generation time, the model starts from pure random noise and iteratively removes noise to produce a clean, realistic output. This approach, grounded in thermodynamic principles, produces exceptionally high-quality samples and has emerged as the leading generative modeling technique, surpassing GANs in image quality and diversity for many applications. Geospatial Applications of Diffusion ModelsDiffusion models are being applied to geospatial challenges that require high-fidelity data generation. Satellite image super-resolution uses diffusion models to generate realistic high-resolution details from coarser inputs, outperforming GAN-based approaches in perceptual quality. Cloud and shadow inpainting applies diffusion models to fill in missing or corrupted regions of optical satellite imagerySatellite ImagerySatellite imagery consists of photographs and data captured by Earth observation satellites orbiting the planet. Thes... with plausible surface details. Conditional diffusion models generate satellite imagery with specified land cover characteristics, enabling targeted data augmentationData AugmentationData Augmentation expands training datasets through transformations like rotation, flipping, color shifting, and crop... for underrepresented classes. Temporal interpolation uses diffusion models to generate synthetic satellite observations between actual acquisition dates, creating denser time series for change monitoring. Advantages Over GANs and Practical ConsiderationsDiffusion models offer more stable training than GANs, avoiding mode collapse and the delicate generator-discriminator balance. They produce more diverse outputs that better represent the true data distribution. However, diffusion models are significantly slower at generation time than GANs, requiring many iterative denoising steps. Recent advances including DDIM sampling, latent diffusion, and consistency models have dramatically accelerated generation speed. For geospatial applications, ensuring that generated imagery is physically consistent with known spectral and spatial properties of real satellite data remains an active research challenge.