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Brown, Christopher F; Brumby, Steven P; Guzder-Williams, Brookie; Birch, Tanya; Hyde, Samantha Brooks; Mazzariello, Joseph; Czerwinski, Wanda; Pasquarella, Valerie J; Haertel, Robert; Ilyushchenko, Simon; Schwehr, Kurt; Weisse, Mikaela; Stolle, Fred; Hanson, Craig; Guinan, Oliver; Moore, Rebecca; Tait, Alexander M
Scientific data, 06/2022, Letnik: 9, Številka: 1Journal Article
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. 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. 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. 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. Furthermore, the continuous nature of the product's outputs enables refinement, extension, and even redefinition of the LULC classification. In combination, these unique attributes enable unprecedented flexibility for a diverse community of users across a variety of disciplines.
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JCR | SNIP | JCR | SNIP | JCR | SNIP | JCR | SNIP |
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in: SICRIS
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