Machine Learning
The Custom Point Cloud Classification tool takes advantage of these signatures to classify specific and unique features in a point cloud. When training the classification, selecting points by identified segment will help to ensure that all points have similar signatures. It’s common to run different segmentation settings to identify different types of objects since they will most likely have unique signatures. The dialog in the image above displays the signature attributes that are common between all of the points that comprise the pylons. Curvature is the most significant attribute, as all of the pylons are flat and end at the 90-degree angle where they connect to the bridge. Creating Training Samples
The Feature model Plot can be used to compare signatures between classes.
Classification signatures are visually displayed in the Feature Model Plot. This bar graph highlights a few of the variables assessed when searching for features in a point cloud. Here, we can see how the Pylon and Streetlight classifications differ from each other per attribute. Once training data has been collected from a portion of the point cloud, the custom class created in Global Mapper Pro can be used to identify features in the data through the Automatic Point Cloud Analysis tool. In the custom classification analysis point cloud segments with value characteristics that fall within the described ranges will be classified accordingly. An enhancement to the Custom Classification Training coming soon in the new version 26.0 release of Global Mapper Pro this fall will be the ability to save and load custom classification definitions into the Automatic Point Cloud Analysis tool. This improvement will allow users to train a custom classification, export the definition to share with other Global Mapper Pro users thus creating a library of niche object classification options within an organization. Also available with the machine learning functionality of custom classifications is the ability to collect samples and train existing classifications: ground, building, etc. Instead of starting from scratch with a new classification, you can train the existing classes to identify specific feature types, such as buildings with unique shapes. In the Classification and Extraction Shared Settings section, expand any Feature Model and click Train to begin the process. Blue Marble Geographics was awarded the Outstanding Innovation in Lidar Award for developing a groundbreaking new functionality in training custom automatic point cloud classifications. Custom Classification through machine learning in Global Mapper Pro opens the door for increasing the accessibility and application of lidar and point clouds in many industries without a high-cost barrier. To learn more about Global Mapper Pro and the advanced point cloud analysis tools offered in the software, visit www.bluemarblegeo.com/global- mapper-pro. Sources: https://www.advancednavigation.com/tech-articles/insights-into- lidar-technology-and-lidar-based-surveying/ https://www.linkedin.com/pulse/lidar-survey-technology-road- infrastructure-santosh-kumar-bhoda/
Segmentation can be used to create separate features in the point cloud based on their signature. Each segment is assigned a different, random color.
A custom classification tool is trained based on selected segments or clusters of points. These selected clusters are assessed to record patterns in the attributes and the bounds or overall shape. When a tool is trained, its signature can be viewed as numerical data or as a bar chart in the feature model plot. Multiple attributes are assessed. The Principal Component attribute measures the 3D shape of the points at the neighborhood level (neighborhood being set by the Resolution field toward the top of the dialog). These look at the feature or segment of points as a whole. For example, in the image below, a new streetlight classification is created to identify these curved, tall structures in the data. When applied, the custom tool will segment the data to find features with a similar shape as determined by Principal Component measurements. Eigentrophy measures the entropy of the point cloud. For example, entropy on a flat surface is very low, but it is high in vegetation. Curvature analyzes the curves created by the points in a local neighborhood. Consistency in curvature values will help indicate that points likely belong to the same object. Normal measures the direction perpendicular to the surface the point is representing. Similar normal values over a local area indicate the points are representing a consistently shaped feature. Feature Model Plot
26 Fall 2024
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