Timeline for Getting LiDAR point cloud point spacing using PDAL
Current License: CC BY-SA 4.0
8 events
| when toggle format | what | by | license | comment | |
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| Oct 23, 2020 at 14:17 | comment | added | Howard Butler |
avg_pt_per_sq_unit == per square unit (tessellated area within inclusions) density == Number of points per square unit (total area) See github.com/PDAL/PDAL/blob/… for the calculations.
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| Oct 16, 2020 at 15:25 | comment | added | Aaron♦ | @auslander your comment sounds like a good candidate for a new question. You'll get a better answer than what I can provide in the comments. | |
| Oct 16, 2020 at 14:34 | comment | added | auslander | @Aaron just piggybacking on OP: how would one get the SRS to be able to then interpret the line/samples? I've looked around on GSE a lot on this and haven't found a real good, stable, updated answer. | |
| Oct 14, 2020 at 17:01 | vote | accept | mdl518 | ||
| Oct 14, 2020 at 16:44 | comment | added | Aaron♦ |
I'm afraid not, the documentation is lacking on this. I have been using lidR in R to work with point cloud data recently so I have not really dug into this tool's outputs. It would be worth investigating which points (e.g first returns) are used to generate the stats as well as the threshold value.
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| Oct 14, 2020 at 16:40 | comment | added | mdl518 | Thanks, Aaron, most helpful! This is exactly what I was looking for, but do you have any insights regarding the difference between the "avg_pt_per_sq_unit" and "density"? I am assuming that we can equate the avg_pt_per_sq_unit to a density (e.g. pts per sq. meter) but I do not know what the density field shown above represents. | |
| Oct 14, 2020 at 5:46 | history | edited | Aaron♦ | CC BY-SA 4.0 |
deleted 5 characters in body
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| Oct 14, 2020 at 5:32 | history | answered | Aaron♦ | CC BY-SA 4.0 |