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3

There are several questions here: Why do you have NAs in the DTM? NAs in the DTM are usually not a big deal. lidR interpolates within the convex hull of the point cloud to ensure to have a DTM in accordance with the point cloud especially with circular plots for example. A raster being rectangular you can have NAs in pixels with no points Why do you have -3....


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I've had lots of success using the point data abstraction library (PDAL) to do this and a host of other operations via the anaconda terminal. You can write a simple pipeline to remove outlier points either statistically or spatially depending on which method you specify. Here is an example script you can store as mypipeline.json format and execute from ...


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I'd say this was a bug in lasmergespatial that occurs when the raster is not stored in memory. The code eventually ends up here: lidR:::lasmergeRasterLayer = function (las, raster) { cells <- raster::cellFromXY(raster, coordinates(las)) return(raster@data@values[cells]) } but extracting the cell values like that only works for in-memory ...


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Your data is not a regular grid, you have missing points. The XYZ format description specifies "no missing value is supported". Below is an image of a small section of the western edge of the data visualised as points showing the missing values. To convert your data to raster, you can use gdal_grid. gdal_grid doesn't support the full range of OGR open ...


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I tried naively to use nearest neighbors to separate the ground and the trees. I iteratively set a point to be a tree, ground, or other type of point. A point is a tree or ground point if a percentage of the k=10 nearest neighbors are tree or ground points. The percentage decreases with each iteration. The initial guess of trees are points with intensity <...


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Is it possible to use other software? I mean CloudCompare. It is free software that will generate geotiff quickly and easily in a few clicks. I made a simple gif with sample asc file:


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This was a bug fixed in version 2.1.4. In short in R when you subset a matrix you get a matrix (that makes sense) but when you subset a single row you get a vector instead of a 1 x n matrix. R's behavior is inconsistent. In rare cases you may have a single remaining triangle in pitfree(). This is why you got an internal error not a matrix. This case is now ...


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You can take you LAS files and add them to an LAS Dataset in ArcGIS. Then, use the LAS Dataset tool to filter the point cloud for different returns. Finally, use the LAS Dataset to Raster tool to create surfaces from your LiDAR data. See this link.


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Try the Set LAS Class Codes Using Features tool. Filter the LiDAR points for non-ground or even first return. The tool will intersect your LiDAR points with your 2D power line feature class. Barring that you could also use the Classification tab->Reassign Classification tool to interactively change the class codes. https://pro.arcgis.com/en/pro-app/help/...


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