Machine Learning
This bridge was surveyed using terrestrial lidar. (Data courtesy of SSMC company)
Automatic Classification Tools For years, Global Mapper has provided an array of built-in automatic classification tools to identify ground, vegetation, building, pole, and wire points. Within the goal of creating additional custom classifications, these automatic tools can be used before training a classification to help filter points that aren’t part of our target features. Different methods are available for each tool to fit different point clouds and feature types better. The Max likelihood method, a machine learning method, was designed for high-resolution and terrestrial point clouds often used in infrastructure management. Max Likelihood is a segmentation-based method. For each classification type, the tool has been tailored to find clusters of points that have the common shapes and characteristics of these features in the point cloud. After using the Automatic Point Cloud Analysis tool to classify ground and vegetation points, the point cloud can be filtered in Global Mapper Pro to expose the infrastructure underneath the vegetation. Excluding irrelevant points from custom classification processing not only increases the accuracy of the classifications but also decreases processing time as there are fewer points to be considered when developing and using a custom classification in Global Mapper Pro. The Custom Classification Tools Default automatic classifications identify objects in point clouds on the basis of their attributes and structures. Each tool is built to look for specific types of structures in the data to find what they are classifying. For instance, the Ground Classification tool will look for characteristics in points such as areas of low elevation change, the last return, etc. Custom classification tools work on a similar principle, but the attributes and structures they search for in the data rely on the point clusters they were trained on. This training data is identified by the user from within the point cloud. Running a Geometric Segmentation prior to training is recommended to begin identifying objects for the selection of training data. Classifications Based on Segmentation Custom classification uses the same machine learning segmentation- based analysis as built-in Max Likelihood classifications to assess point cloud characteristics and find commonalities among the points that make up an object. For example, to segment paint stripes on the road, a user would look for points that make up a flat surface, have the same color, normal values, etc. This method operates on the assumption that each object in the point cloud, each cluster of points identified with segmentation analysis, has a signature made up of attributes and/ or structures that differentiate it from its neighbors.
With advancements in technology , point clouds have emerged as a popular survey method for infrastructure management. They provide the ability to access difficult-to-reach areas remotely. Through analysis, visualization tools, and machine learning, point clouds can be analyzed to understand and estimate various conditions and characteristics of a survey site. Point cloud surveys often contain thousands, if not millions of points, capturing detailed positional information, attribute data, and more for a survey site. Manually deriving information from these large data sets can be an arduous task. Global Mapper Pro, an all-in-one geospatial software, simplifies this task with tools for point cloud visualization, classification, and analysis. Global Mapper Pro’s Automatic Point Cloud Analysis tool identifies standard features such as ground and vegetation adding incredibly valuable structural meaning to any point cloud. When it comes to surveys of structures and many other niche objects, automatic classification tools may not be sufficient in such industry-specific scenarios. In such cases, machine learning techniques can be applied to customize automatic unique features that are not part of the standard feature set. Global Mapper Pro’s Custom Classification Training tool, an extension of the Automatic Point Cloud Analysis tool, is an award-winning method that enables the creation of custom classifications that can automatically identify target features within a point cloud. The terrestrial lidar scan of a bridge used in this example was collected by one of our users, Southeastern Surveying and Mapping Corporation. This high-resolution dataset cleanly captured all angles of the bridge, providing a clear representation of the features.
In this point cloud, the vegetation classification tool was used to identify the grass and bushes. Classified points can be easily filtered out, to expose the infrastructure underneath.
25
Fall 2024
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