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

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.·2022·Scientific Data
DOI 10.1038/s41597-022-01307-4PMC PMC9184477
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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. 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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  • World Cover and Dynamic World cover only basic land cover categories (11 and 9 classes) and have limited ability to depict urban environments.

    “Although they are highly accurate and real-time, they cover only basic land cover categories (11 and 9 classes) and have limited ability to depict urban environments.”

    From Enabling country-scale land cover mapping with meter-resolution satellite imagery (2023) · cites this paper as Brown et al., 2022

    The Methods taxonomy (page unavailable) lists nine broad Dynamic World classes, including a single built-area class. Usage Notes says built area, as a mixed class, had lower accuracy in one derived-product assessment, but Technical Validation reports high agreement for built area in single-image predictions. The paper does not establish WorldCover’s 11-class count or assess its ability to depict urban environments, so it supports only the Dynamic World portion of the claim. (LLM, conf=0.85)

    Experimental — accuracy not yet measured

  • Sentinel satellite data are utilized in the state-of-the-art global land cover mapping projects, ESA's World Cover and Google's Dynamic World.

    “Sentinel satellite data are utilized in the state-of-the-art global land cover mapping projects, ESA’s World Cover (Zanaga et al., 2021) and Google’s Dynamic World (Brown et al., 2022).”

    From Enabling country-scale land cover mapping with meter-resolution satellite imagery (2023) · cites this paper as Brown et al., 2022

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Citations

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Metadata

Authors
Brown, C. F., Brumby, S. P., Guzder-Williams, B., Birch, T., Hyde, S. B., Mazzariello, J., Czerwinski, W., Pasquarella, V. J., Haertel, R., Ilyushchenko, S., Schwehr, K., Weisse, M., Stolle, F., Hanson, C., Guinan, O., Moore, R., & Tait, A. M.
ORCID
Venue
Scientific Data
Publisher
Springer Science and Business Media LLC
Published
2022-06-09
Volume
9
Issue
1
ISSN
2052-4463
DOI
10.1038/s41597-022-01307-4
PMC
PMC9184477
Cited by
1,154
References
1
Language
en
Licence
Funders
  • Google
  • Gordon and Betty Moore Foundation

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