Create a DTM without noise (man-made or vegetation)

The article explains how to remove noise from LiDAR datasets for DTM creation using the Survey Point Cloud Ground Filter tool, which separates ground and non-ground points. It compares Cloth Simulation and Simple Morphological Filter methods, suggesting testing both. Additionally, it discusses using aerial imagery to color-code points via RGB attributes for noise filtering. Links to online help and training resources are provided.

Imagine you receive a LiDAR dataset or survey points from a survey drone to create a DTM with aerial imagery. In a scenario where you have significant noise and unwanted points, such as man-made features like buildings, roads, vehicles, or noise from trees that can create spikes in the wireframe, it becomes crucial to remove such noise efficiently.

One effective tool for this purpose is Survey > Point Cloud > Ground Filter, which separates a LiDAR point cloud into ground and non-ground measurements, effectively removing noise. As shown below, the LiDAR dataset includes buildings and vegetation. Using this tool, you can generate two outputs: a noise-free LiDAR dataset for DTM creation and a noisy dataset that includes features such as buildings and vegetation.

Depending on your scenario, you can choose between Cloth Simulation or the Simple Morphological Filter (SMRF). In many cases, Cloth Simulation provides smoother results, but you can test both methods on a small sample of your data to determine which works better for your specific requirements.

Another useful approach is leveraging aerial imagery to color-code your input points by creating an RGB or Color attribute using Survey > Point Cloud > Generate Attributes. This process requires both your LiDAR points and an orthophoto, allowing the software to classify points based on their corresponding color in the orthophoto. Once classified, you can filter these attributes to remove specific types of noise.

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