CLC+ Backbone: Set the Scene in Copernicus for the Coming Decade

M. Probeck, Inés Ruíz, G. Ramminger, C. Fourie, Pirmin Maier, Martin Ickerott, Cornelia Storch, A. Homolka, S. J. Muller, Himanshu Tiwari, A. Stumpf, Sooyeon Chun, C. Mattos, Amelie Lindmayer, Fahad Jahangir, Pilar Endara, F. Berndt, M. Dohr, W. Kapferer, C. Schleicher, S. Ralser, Florian Innerbichler, M. Riffler, Martin Siklar, Dorothea Aifantopoulou, Sideris Paralykidis, Camille Pinet, G. Jaffrain, A. D. Federico, M. Corsi, T. Langanke, H. Dufourmont
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引用次数: 4

Abstract

With the CLC+ product suite as part of the Copernicus Land Monitoring Service (CLMS), the European Environment Agency (EEA) has initiated a true paradigm change in European land cover/land use monitoring, building on the 30-years-long rich legacy of the European CORINE Land Cover (CLC) flagship product. The CLC+ Backbone, as first component of the upcoming CLC+ era, will feature an object-oriented wall-to-wall high-resolution inventory of European land cover and its characteristics in unprecedented quality and detail, for the reference year 2018. It will comprise a pan-European combined “hardbone” and “soft-bone” segmentation of vector-based stable landscape objects and a raster-based classification of 11 EAGLE compliant land cover classes at 10m spatial resolution. To this end, a combination of image segmentation and Deep Learning approaches are implemented within a cloud-based infrastructure for a fully integrated analysis of optical/radar time series of Sentinel-1/-2 satellite imagery and auxiliary data. Vector and raster datasets will be fused into a fully attributed, 18 land cover class, vector product with 0.5 ha minimum mapping unit (MMU), additionally incorporating a multitude of further information layers derived from satellite data and various other Copernicus products.
CLC+ Backbone:为未来十年设定哥白尼的场景
随着CLC+产品套件作为哥白尼土地监测服务(CLMS)的一部分,欧洲环境署(EEA)在欧洲CORINE土地覆盖(CLC)旗舰产品长达30年的丰富遗产的基础上,启动了欧洲土地覆盖/土地利用监测的真正范式变革。作为即将到来的CLC+时代的第一个组成部分,CLC+骨干将以2018年为参考年,以前所未有的质量和细节提供面向对象的全欧洲土地覆盖及其特征的高分辨率库存。它将包括泛欧基于矢量的稳定景观物体的“硬骨”和“软骨”分割,以及基于栅格的11个符合EAGLE标准的10米空间分辨率土地覆盖类别分类。为此,在基于云的基础设施中实现了图像分割和深度学习方法的组合,以便对Sentinel-1/ 2卫星图像和辅助数据的光学/雷达时间序列进行全面集成分析。矢量和栅格数据集将融合成一个全属性的18个土地覆盖类矢量产品,具有0.5公顷的最小测绘单位(MMU),另外还包含来自卫星数据和各种其他哥白尼产品的大量进一步信息层。
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1.20
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