The Dynamic World project can provide details on how land use is affected by climate change and human activity.
Robert Bye / UnsplashToday, Google Earth is launching a project called Dynamic World, a new endeavor that creates maps that come paired with a novel deep learning AI model. It’s able to classify land cover by type at a resolution of 10 meters, or 32 feet. That means each pixel covers about 10 meters of land. For comparison, previous state-of-the-art technology had a 100 meter resolution .
Dynamic World is a way for people to observe from space the myriad ways land cover changes on Earth, whether it’s from natural seasonal changes, climate change-exacerbated storms and disasters, or long-term changes that are caused by human activity such as clearing of wild habitats for crops, cattle, or logging. Experts and researchers can use this new project to understand how land cover changes naturally, and flag when some unexpected changes appear to be taking place.
The map model, which draws satellite imagery from the European Space Agency’s Sentinel-2, can update its data stream for global land cover monitoring every 2 to 5 days. In fact, around 12 terabytes of data comes from the Sentinel-2 satellite every day. From there, it goes into Google’sand Google Earth Engine, a cloud platform built for organizing and relaying Earth observations and environmental analytics.
In order to be able to automatically label how the land represented in all those satellite images is used, Google needed the help of artificial intelligence. The land cover labeling AI they developed as part of this project was trained on 5 billion pixels labeled by human experts . In the training data, they identified pixels in Sentinel-2 images and what land cover class they were .
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