多样性(控制论)
计算机科学
土地覆盖
灵活性(工程)
测距
可扩展性
封面(代数)
遥感
产品(数学)
数据挖掘
数据科学
土地利用
人工智能
数据库
地理
电信
土木工程
工程类
几何学
统计
数学
机械工程
作者
Christopher F. Brown,Steven P. Brumby,B. P. Guzder-Williams,Tanya Birch,Samantha Brooks Hyde,Joseph C. Mazzariello,Wanda Czerwinski,Valerie J. Pasquarella,Robert Haertel,Simon Ilyushchenko,Kurt Schwehr,Mikaela Weisse,Fred Stolle,Craig Hanson,Oliver Guinan,Rebecca Moore,Alex Tait
标识
DOI:10.1038/s41597-022-01307-4
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) 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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