谷歌地球高程数据精度研究

IF 0.7 Q4 ASTRONOMY & ASTROPHYSICS
K. El-Ashmawy
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引用次数: 35

摘要

数字高程模型(dem)是许多工程应用所需的高程数据的重要来源。等高线、坡向图是它们众多用途的一部分。此外,dem经常用于地理信息系统(GIS),并且是数字制作地形图的最常见基础。本文提出了一种利用谷歌地球高程数据生成DEM的方法,该方法简单、免费。该案例研究包括埃及北部海滩的三个不同的小区域。利用均方根误差(RMSE)、平均误差(ME)和最大绝对误差(MAE)对谷歌地球反演高程数据进行了精度分析。所有这些精度统计数据都是使用案例研究的每个区域的200个参考点的地面坐标计算的。参考资料采用全站仪调查法收集。结果表明,所制备的DEM精度适合某些特定的工程应用,但不足以满足非常精确的工程研究所需的精细/小尺度DEM的标准。获得的高差较小的地形精度可用于编制大面积地籍图、城市规划图或土地分类图。一般而言,谷歌地球高程数据只能用于调查和初步研究,成本较低。因此,谷歌Earth的用户在使用之前必须通过与参考数据的比较来检验高程数据的准确性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Investigation of the Accuracy of Google Earth Elevation Data
Abstract Digital Elevation Models (DEMs) comprise valuable source of elevation data required for many engineering applications. Contour lines, slope - aspect maps are part of their many uses. Moreover, DEMs are used often in geographic information systems (GIS), and are the most common basis for digitally-produced relief maps. This paper proposes a method of generating DEM by using Google Earth elevation data which is easier and free. The case study consisted of three different small regions in the northern beach in Egypt. The accuracy of the Google earth derived elevation data are reported using root mean square error (RMSE), mean error (ME) and maximum absolute error (MAE). All these accuracy statistics were computed using the ground coordinates of 200 reference points for each region of the case study. The reference data was collected with total station survey. The results showed that the accuracies for the prepared DEMs are suitable for some certain engineering applications but inadequate to meet the standard required for fine/small scale DEM for very precise engineering study. The obtained accuracies for terrain with small height difference can be used for preparing large area cadastral, city planning, or land classification maps. In general, Google Earth elevation data can be used only for investigation and preliminary studies with low cost. It is strongly concluded that the users of Google Earth have to test the accuracy of elevation data by comparing with reference data before using it.
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CiteScore
1.00
自引率
11.10%
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