![]() Read on to learn more about s2cloudless and how you can start working with it. We hope you’ll find it just as beneficial in your own work. The s2cloudless dataset provides a flexible method to accurately mask cloudy pixels in Level 1C (TOA) and 2A (SR) imagery for generating cloud-free composites and running classification procedures.Ĭloud masking is an essential step in our work to map the world the s2cloudless dataset is making it easier for us to include Sentinel-2 in these efforts. By uniting Sentinel Hub’s algorithm with Google’s computing resources, we have calculated per-pixel cloud probability for the entire Sentinel-2 archive at 10 m scale each new image added to the Earth Engine catalog is also accompanied by an s2cloudless image. ![]() To help solve this problem when using Sentinel-2 imagery in Earth Engine, we have rolled out a new collection of cloud probability images calculated with the s2cloudless algorithm. They can interfere with machine learning tools you’re using to identify everything from fields to forests and prevent you from easily assembling cloud-free composites. But if you’re studying conditions on the ground using satellite imagery, clouds tend to get in the way of your work. More accurate and flexible cloud masking for Sentinel-2 imagesīy Justin Braaten, Kurt Schwehr, and Simon Ilyushchenko on behalf of the Earth Engine Data teamĬlouds are wonderful to look at if you’re taking a leisurely walk and staring at the sky.
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