MESA: Text-Driven Terrain Generation Using Latent Diffusion and Global Copernicus Data

It is a complex and time-consuming task, particularly when it involves large-scale landscapes, which are getting more common with the current boom in popularity of open world games. The current state-of-the-art (SOT A) in terrain modeling relies mainly on procedural and simulation methods [8], which rarely scale well beyond a certain point (compute expensive or lack of realism) and can easily fail to capture the variety of the landscape the world offers. The recent advances in generative machine learning and especially in the area of diffusion models have paved the way for models that can learn a representation of Earth's landscapes directly from real terrain data. By abstracting the complexity of the underlying physical processes, generative models can learn to reproduce patterns and mutual dependencies between visual features, which can lead to* First author high levels of perceptual realism. This work explores the potential of following a similar data-centric methodology for a joint domain of terrain surface model and optical reflectance.