链接线段:使用 Hough 变换聚类线段和中尺度特征提取

Allison Kubo Hutchison, L. Karlstrom, Tushar Mittal
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引用次数: 0

摘要

线性特征分析在地理空间应用中发挥着基础性作用,从检测基础设施网络到描述地质构造,不一而足。本文介绍的 linkinglines 是一个开源 Python 软件包,专门用于地理空间数据中线性结构的聚类和特征提取。我们的软件包利用图像处理中常用的 Hough 变换,在 Hough 空间中对线段进行聚类,然后提供独特的特征提取方法和可视化。linkinglines 可帮助不同领域的研究人员、数据科学家和分析师高效地处理、理解线性特征并从中提取有价值的见解,从而有助于做出更明智的决策和加强数据驱动的探索。我们利用 linkinglines 绘制了与 Kubo Hutchison 等人(2023 年)的大型火成岩矿带相关的堤群地图,其中包含数千个线段。
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LinkingLines: Using the Hough Transform to Cluster Line Segments and Mesoscale Feature Extraction
Linear feature analysis plays a fundamental role in geospatial applications, from detecting infrastructure networks to characterizing geological formations. In this paper, we introduce linkinglines , an open-source Python package tailored for the clustering and feature extraction of linear structures in geospatial data. Our package leverages the Hough Transform, commonly used in image processing, performs clustering of line segments in the Hough Space, and then provides unique feature extraction methods and visualization. linkinglines em-powers researchers, data scientists, and analysts across diverse domains to efficiently process, understand, and extract valuable insights from linear features, contributing to more informed decision-making and enhanced data-driven exploration. We have used linkinglines to map dike swarms with thousands of segments associated with Large Igneous Provinces in Kubo Hutchison et al. (2023).
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