Super-Resolution
Nakuru, Kenya
Super-Resolution allows a very high level of detail compared to Sentinel-2 images.
Introduction
Global low resolution satellite imagery is available on a weekly basis, but public high resolution imagery is very limited. High resolution imagery can help with tasks such as post-disaster building damage estimation and crop type classification, but since it is available infrequently, we cannot scale up promising AI methods to automate these tasks, especially in developing countries where it is needed most.
We have trained super resolution models to generate high resolution imagery on a global scale.
The Satlas Map allows users to explore globally generated high resolution imagery for 2023.
Frequent Updates
Super-Resolution is generated from 2023 Sentinel-2 imagery, and is sometimes more up-to-date than Google Maps. This example of Cebu City, Phillippines shows evidence of coastal infrastructure in both Sentinel-2 and the generated Super-Resolution but not in the Google Maps image.
Change Over Time
With increased resolution and cloud-free views, Super-Resolution's enhanced imagery makes it easier to see change over time.
Super-Resolution Model
The Super-Resolution imagery is generated using an adaptation of the ESRGAN model. It is trained on pairs of corresponding Sentinel-2 (low resolution) and NAIP (high resolution) images. Our model inputs a sequence of between 6-18 Sentinel-2 images for each location.
The training and inference code is open source and can be found at our Github. We note that AI models are not perfect and there are instances where the super-resolution outputs are incorrect.