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<p>Light Detection and Ranging (LiDAR) technology has experienced a progressive increase in popularity over the past decade in the field of geoscience, largely due to its widespread use in applications ranging from basic cartography and autonomous navigation systems to fields of expertise such as precision agriculture, environmental management, and road safety, among others. Despite its growing adoption and relevance, there is a lack of free and open-source applications that facilitate LiDAR data management, from processing to visualization and metric data generation. This work proposes a free-to-use suite of tools to complement the functions of current applications involved in the processing and visualization of LiDAR points in order to optimize user workflows. Its implementation consists of a QGIS add-on, given the native functionalities already present in the selected GIS and its open-source nature. Likewise, it will be valuable for testing its functionality on sample point clouds and gathering feedback on its performance.</p>

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MyLiDAR: Improving LiDAR data processing and analysis with a comprehensive open-source QGIS plugin

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月22日

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  • Pablo Fernández-González,
  • Aurora Cuartero,
  • Fernando Broncano,
  • Pablo G. Rodríguez

摘要

Light Detection and Ranging (LiDAR) technology has experienced a progressive increase in popularity over the past decade in the field of geoscience, largely due to its widespread use in applications ranging from basic cartography and autonomous navigation systems to fields of expertise such as precision agriculture, environmental management, and road safety, among others. Despite its growing adoption and relevance, there is a lack of free and open-source applications that facilitate LiDAR data management, from processing to visualization and metric data generation. This work proposes a free-to-use suite of tools to complement the functions of current applications involved in the processing and visualization of LiDAR points in order to optimize user workflows. Its implementation consists of a QGIS add-on, given the native functionalities already present in the selected GIS and its open-source nature. Likewise, it will be valuable for testing its functionality on sample point clouds and gathering feedback on its performance.