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Automated Annotation of Satellite Imagery using Model-based Projections

机译:使用基于模型的投影自动注释卫星图像

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GeoVisipedia is a new and novel approach to annotating satellite imagery. It uses wiki pages to annotate objects rather than simple labels. The use of wiki pages to contain annotations is particularly useful for annotating objects in imagery of complex geospatial configurations such as industrial facilities. GeoVisipedia uses the PRISM algorithm to project annotations applied to one image to other imagery, hence enabling ubiquitous annotation. This paper derives the PRISM algorithm, which uses image metadata and a 3D facility model to create a view matrix unique to each image. The view matrix is used to project model components onto a mask which aligns the components with the objects in the scene that they represent. Wiki pages are linked to model components, which are in turn linked to the image via the component mask. An illustration of the efficacy of the PRISM algorithm is provided, demonstrating the projection of model components onto an effluent stack. We conclude with a discussion of the efficiencies of GeoVisipedia over manual annotation, and the use of PRISM for creating training sets for machine learning algorithms.
机译:Geovisipedia是一种新的和新颖的注释卫星图像的方法。它使用wiki页面注释对象而不是简单的标签。使用Wiki页面来包含注释对于在工业设施等复杂地理空间配置的图像中的注释对象特别有用。 Geovisipedia使用棱镜算法将应用于一个图像的项目注释到其他图像,因此实现了普遍存​​在的注释。本文推出了棱镜算法,它使用图像元数据和3D设施模型来创建每个图像唯一的视图矩阵。 View Matrix用于将模型组件投影到蒙版上,该掩码将组件与它们所代表的场景中的对象对齐。 Wiki页面与模型组件相关联,这些组件又通过组件掩码连接到图像。提供了棱镜算法的效果的图示,将模型组分的投影显示在流出物堆叠上。我们讨论了Geovisipedia对手动注释的效率,以及棱镜为机器学习算法创建训练集的使用。

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