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

By Christopher F. Brown, S. Brumby, B. Guzder-Williams, Tanya Birch, S. B. Hyde, Joseph C. Mazzariello, Wanda Czerwinski, Valerie J. Pasquarella, R. Haertel, S. Ilyushchenko and 7 more. Scientific Data, 2022

955 citations recorded by the source corpus.

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. Measurement(s) land use • land cover Technology Type(s) deep learning Measurement(s) land use • land cover Technology Type(s) deep learning

Subfields: Ecology, Environmental Engineering, Media Technology

View Paper: https://www.semanticscholar.org/paper/4236d3cd173f87ae743408caa51eb8095496ef92

How researchers see this paper

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In Ecology

1. Tirthankar Chakraborty
2. T. Simensen
3. G. Singh
4. David N. Barton
5. Zander S. Venter

In Environmental Engineering

1. Tirthankar Chakraborty
2. T. Simensen
3. G. Singh
4. David N. Barton
5. Zander S. Venter

Citations in the corpus

• Global land use / land cover with Sentinel 2 and deep learning (2021, methodology): “For this, we use over 24,000 5km x 5km image chips that were handlabeled into ten classes (water, trees, grass, flooded vegetation, crops, scrub/shrub, built area, bare ground, snow/ice, and clouds) and collected across 14 major biomes (as defined in [7]) employing a random stratified sampling appro”
• Global 10 m Land Use Land Cover Datasets: A Comparison of Dynamic World, World Cover and Esri Land Cover (2022, methodology): “Since 2021, there have been three global Sentinel-based 10 m LULC maps released, including Google’s Dynamic World (DW) [23], ESA’s World Cover 2020 (WC) [24] and Esri’s 2020 Land Cover (Esri) [25].”