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Bogus XY coordinates, radii in inventory #28
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Hi there, it's hard to evaluate your issue without a more comprehensive context. To understand it I need the complete workflow before applying the 1 ha plots are quite large, and if the point cloud is not thoroughly treated before getting to the inventory part the results can vary widely across the point cloud. Preprocessing is highly dependent on the 3D scanner, survey type and the algorithm you choose further on. For instance, TLS and MLS point clouds usually have very heterogeneous point densities, so it's a good practice to standardize those point cloud densities to get a similar amount of pts/m³ on all point cloud regions, and/or to split the dataset into tiles and process them separately - which is easier to inspect visually. All steps are really important to get good inventory/segmentation results, with the most critical ones probably being the By setting too strict parameters at those steps, you might omit many trees in the results, but if you pass too flexible parameters (low density criteria, wide expected angle/diameter intervals, large pixel/voxel sizes) you might get too many false positives, so the trial and error phase should focus on get a good balance on those results. Just looking at the print from your console I'd say the results are weird for a few reasons:
TL;DR And finally, if you provide a more complete code snippet (from reading the point cloud up to the inventory step) and some data sample I can make a better assessment and help you with the specifics. Cheers! |
Thanks Tiago for your response. Code snippet is below: #Load normalised plot point cloud |
Hi there,
What would be causing bogus XY coordinates in the inventory? Also provides an incredibly large radius. See attached screenshot, trees 10, 1153 and 1154.
XY values are UTM. Occurs regardless of data set (TLS or drone LS). All data sets are filtered for outliers and are clipped to a 1 ha plot.
Cheers, Tim
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