Color Consideration in Global Mapper’s Point Cloud Segmentation Tool

The rapid expansion of 3D data collection and analysis is being seen throughout many industries, driven in large part by the desire to view and explore data that more accurately represents the 3D world. Data collection methods and software processing programs, such as Global Mapper, are rapidly evolving to better support working with this type of data. Containing a suite of advanced geospatial analysis tools, Global Mapper Pro supports both manual and automatic point cloud classification, including a customizable Point Cloud Segmentation tool that effectively identifies clusters of points representing unique features or structures.

Introduced in the Pro version of Global Mapper 23, Segmentation by Spectral Graph Partitioning provides a powerful way to control how point clouds are analyzed in order to better identify objects represented in the data. Based on user-defined parameters, attributes of point returns are considered to find and label clusters or segments of points with similar characteristics. The segments are subsequently labeled with a segment ID and a unique color is applied to each cluster providing a clear visual distinction between adjacent segments. These clusters can be collectively selected and manually reclassified if necessary. 

Providing enhanced control and flexibility in feature identification, the Segmentation tool provides the option to choose which attributes of the point returns to consider in the segmentation process, and to apply a weight value that reflects the relative importance of each variable. The position of each of the returns is always considered to ensure segments are spatially connected, but attributes such as normal direction, intensity, return number, curvature, and color can also be considered. The color or RGB value associated with each point is a new addition to the list of attributes available for consideration in the Segmentation tool as lidar and photogrammetric point clouds often include a true color value.

Color can be a helpful characteristic for analysis when looking to differentiate between different land cover areas in order to apply custom classifications. In the following example, a park area containing areas of open grass and paved pathways will be analyzed with the objective of identifying and classifying points representing the paved pathway areas.

When considering this study area, the pathways are not raised and do not have any three-dimensional characteristics differentiating them from surrounding areas of grass. However, the lighter color of the pathways can be used in the segmentation tool to clearly identify them. 

Before embarking on the segmentation analysis, Global Mapper Pro’s Automatic Ground Classification tool is used, and the point cloud is filtered to remove the non-ground points, such as those representing trees, from the segmentation analysis.

Using only the position, intensity, and color attributes, with a higher weighting value applied to the color variable, the segmentation analysis is executed, and sections of the paved pathways are identified. With unique segment IDs applied, the Select Lidar Segments tool is then used to manually select all of the points in the major pathway segments. These individual segments are merged using the Segment ID(s) Settings in Global Mapper’s Segmentation tool. 

This new button and corresponding dialog in the Segmentation tool allows users to combine segments under a single ID value, which simplifies visualization and the process of excluding specific segments from subsequent analyses. After merging the segments for the larger paths, the new ID is noted allowing this segment to be excluded from the next phase of the segmentation analysis, which aims to identify smaller paths in the park. 

In the second phase of the analysis, color is still heavily weighted, but the minimum number of required points in each segment and the connectivity threshold are both reduced, resulting in smaller segments containing fewer points. The new segments identified represent the smaller paths in the point cloud and are selected along with the previously merged segment to be manually classified. 

The advanced controls available in Global Mapper’s Segmentation tool streamlines the process of identifying and selecting points that share common characteristics. The addition of color in the segmentation process offers a powerful new way to isolate and manually reclassify clusters of adjacent points based on their shared visual traits. If you would like to try the new Point Cloud Segmentation in Global Mapper Pro, download a 14-day free trial today! If you have any questions, don’t hesitate to get in touch. 

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Point Cloud Segmentation Analysis in Global Mapper

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