Generating Synthetic Multispectral Satellite Imagery from Sentinel-2

Understand

Multi-spectral satellite imagery provides valuable data at global scale for many environmental and socio-economic applications.

• Building supervised machine learning models based on these imagery, however, may require ground reference labels which are not available at global scale.
• Here, we propose a generative model to produce multi-resolution multi-spectral imagery based on Sentinel-2 data.
• The resulting synthetic images are indistinguishable from real ones by humans.