文章基本信息

标题：Dynamic World, Near real-time global 10 m land use land cover mapping
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作者：Christopher F.Brown ; Steven P.Brumby ; Brookie Guzder-Williams 等
期刊名称：Scientific Data
电子版ISSN：2052-4463
出版年度：2022
卷号：9
期号：1
页码：1-17
DOI：10.1038/s41597-022-01307-4
语种：English
出版社：Nature Publishing Group
摘要：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) classifcation 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 frst-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-specifed date ranges . Furthermore, the continuous nature of the product’s outputs enables refnement, extension, and even redefnition of the LULC classifcation . In combination, these unique attributes enable unprecedented fexibility for a diverse community of users across a variety of disciplines .