Dynamic World, Near real-time global 10 m land use land cover mapping

Abstract Unlike satellite images, which are typically acquired and processed in near-real-time, global land cover products have historically been produced on an annual basis, often with substantial lag times between image processing and dataset release. Additionally, the continuous nature of the product’s outputs enables refinement, extension, and even redefinition of the LULC classification.

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• 1 We developed a new automated approach for globally consistent, high resolution, near real-time (NRT) land use land cover (LULC) classification leveraging deep learning on 10 m Sentinel-2 imagery.
• 2 We utilize a highly scalable cloud-based system to apply this approach and provide an open, continuous feed of LULC predictions in parallel with Sentinel-2 acquisitions.
• 3 This first-of-its-kind NRT product, which we collectively refer to as Dynamic World, accommodates a variety of user needs ranging from extremely up-to-date LULC data to custom global composites representing user-specified date ranges.

This research advances how AI systems learn, reason, and solve problems — with direct implications for automation and scientific discovery.

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