![]() ![]() The very first step in post-processing is to identify and eliminate noise points, which are extremely high or low points outside the range of realistic elevations for the project area. Features above ground (including buildings, tree crowns, cars, poles, bridges…).If the end-result of the project is to produce a bare earth terrain model, the following categories are recommended: In the LAS format, class types can be classified properly, even user-defined (attachment – LAS 1.4). Layer relates to return number, but takes one step toward elevation classification. Return number is simply first, second, third, fourth, etc., depending on the number of returns recorded by the particular sensor (attachment - Major LiDAR Sensors). Usually, returns are flagged several ways: by return number, by layer, or by type classes. Specialists only attribute each point with various flags that reflect attributes or characteristics of that point. Never delete points, add points, or change the elevation of points in LAS data, when working in the LAS format. In practice, the data should be divided into smaller blocks of around 5- 10 million points, d ue to the limits of current operating systems and computing. Starting final classification to ground, vegetation, building etc.Classifying ground points back to default. ![]() Solving heading, roll and pitch for whole data set.Classifying ground points separately after each flightline.Dividing data into smaller geographical regions (blocks).Typical steps in their different working order are as follows: Generally, l aser data processing means numerous 'rigorous' working steps. With good acquisition plan, highly qualified field personnel consisting of professional licensed land surveyors, licensed pilots and LiDAR technicians operate the system to ensure quality results from each flight to meet project requirements. LiDAR acquisition systems capable of recording lidar data with sufficient accuracy over a range of altitudes should be required. LAS Point Clouds data can be accurate and reliable, ONLY when following rigorous quality control standards during acquisition and processing in operation. ![]()
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