革命性的空间数据分析:揭示批坐标转换的前沿方法

Waruna Buddhika , Kumesha Premawansha , Thushara R. Bandara , Lakdinu Samaranayake , Viraj Dayananda , Chameera Mudannayaka , Shyama Priyadarshani
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引用次数: 0

摘要

空间数据已成为各个学科不可或缺的一部分,并提供了至关重要的见解。这些数据与坐标和不同的坐标系相关联。然而,地理空间数据格式的多样性和不同的坐标系统对协调它们进行分析提出了挑战。本研究解决了斯里兰卡常用坐标系统批量转换的改进方法的迫切需要。首先,我们研究了不同的坐标变换系统,并确定了它们的局限性。随后,我们提出了各种系统之间无缝坐标转换的综合程序。为了演示我们的方法的实际应用,我们开发了一个用户友好的桌面应用程序,能够同时将输入坐标转换为多个系统。此应用程序为不熟悉复杂地理信息系统(GIS)应用程序和数据转换的用户简化了流程。我们通过将使用我们的应用程序转换的输出坐标与从ArcGIS和epsg.io等已建立的应用程序获得的坐标进行比较来验证它们。根据均方根误差(RMSE)和平均绝对误差(MAE)对结果进行了评估,结果表明准确度很高,最大RMSE约为0.013,最大MAE约为0.008。性能评估表明,我们的方法是非常有效的,优于ArcGIS和epsg。IO分别提高了40倍和60倍。此外,提议的管道具有开发web应用程序、移动应用程序和流行GIS平台(如ArcGIS和QGIS)插件的基础设施的潜力。
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Revolutionizing spatial data analysis: unveiling a cutting-edge approach for batch coordinate transformation

Spatial data have become indispensable across various disciplines and provide crucial insights. These data are associated with coordinates and different coordinate systems. However, the diversity of geospatial data formats and disparate coordinate systems present challenges in harmonizing them for analysis. This study addresses the pressing need for an improved approach to the batch transformation of commonly used coordinate systems in Sri Lanka. First, we examine different coordinate transformation systems and identify their limitations. Subsequently, we present a comprehensive procedure for seamless coordinate transformations between various systems. To demonstrate the practical applications of our approach, we have developed a user-friendly desktop application capable of simultaneously converting input coordinates into multiple systems. This application streamlines the process for users unfamiliar with sophisticated geographic information system (GIS) applications and datum transformations. We validate the output coordinates transformed using our application by comparing them with those obtained from established applications such as ArcGIS and epsg.io. The results, which have been assessed based on the root mean squared error (RMSE) and mean absolute error (MAE), indicate high levels of accuracy, with a maximum RMSE of approximately 0.013 and a maximum MAE of approximately 0.008. A performance evaluation reveals that our approach is exceptionally efficient, outperforming ArcGIS and epsg.io by 40x and 60x, respectively. Moreover, the proposed pipeline holds potential as an infrastructure for developing web applications, mobile applications, and plugins for popular GIS platforms such as ArcGIS and QGIS.

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