Raptor: Large Scale Analysis of Big Raster and Vector Data
Raptor: Large Scale Analysis of Big Raster and Vector Data
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DOI：
10.14778/3352063.3352107
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发表时间：
2019-08-01
影响因子：
2.5
通讯作者：
Mokbel, Mohamed F.
中科院分区：
文献类型：
--
作者：
Singla, Samriddhi;Eldawy, Ahmed;Mokbel, Mohamed F.
随着遥感数据量的增加，人们一直在努力有效地处理这些数据，以帮助生态学家和地理学家回答问题。然而，他们通常需要将这些数据与矢量数据（例如城市边界）结合起来处理。现有的工作需要将一个数据集转换为另一种表示，这对于大型数据集来说效率极低。在这个演示中，我们专注于区域统计问题，它为矢量层中的每个多边形计算栅格层上的统计信息。我们展示了三种方法，基于矢量、基于光栅和基于猛禽的方法。后者是最近在不需要任何转换的情况下结合栅格和矢量数据的一项努力。这个演示将允许用户使用这三种方法中的任何一种来运行他们自己的查询，并根据不同的栅格和矢量数据集大小来观察它们的性能差异。
With the increase in amount of remote sensing data, there have been efforts to efficiently process it to help ecologists and geographers answer queries. However, they often need to process this data in combination with vector data, for example, city boundaries. Existing efforts require one dataset to be converted to the other representation, which is extremely inefficient for large datasets. In this demonstration, we focus on the zonal statistics problem, which computes the statistics over a raster layer for each polygon in a vector layer. We demonstrate three approaches, vector-based, raster-based, and raptor-based approaches. The latter is a recent effort of combining raster and vector data without a need of any conversion. This demo will allow users to run their own queries in any of the three methods and observe the differences in their performance depending on different raster and vector dataset sizes.