Better Data for Satellite Super Resolution

Reliable satellite data is needed for many large-scale tasks in urban planing, agriculture, and disaster relief. However, high resolution satellite data is restricted or expensive. ESA's Sentinel-2 provides free satellite data with global coverage but only at a coarse level of detail. In this work we use super-resolution models trained to create high-resolution versions of Sentinel-2 data. We compare the feasibility of various CLIP embeddings to evaluate similarity between hallucinated satellite data and extend the existing S2-NAIP dataset. We automatically clean unreliable data and add new NIR band data. Our experiments show clear improvement in fidelity and quality of single image cross-sensor super resolution for satellite images.