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<h2>DESCRIPTION</h2>
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-The <em>r.in.lidar</em> module will load and bin LAS LiDAR point clouds
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+The <em>r.in.lidar</em> module loads and bins LAS LiDAR point clouds
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into a new raster map. The user may choose from a variety of statistical
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methods in creating the new raster.
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<p>
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-Please note that the current region extents and resolution are used for
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-the import. It is therefore recommended to first use the <em>-s</em>
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-flag to get the extents of the LiDAR point cloud to be imported, then
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-adjust the current region accordingly, and only then proceed with the
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-actual import.
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+Since the creation of raster maps depends on the computational
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+region settings (extent and resolution), as default the current
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+region extents and resolution are used for the import. When using
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+the <em>-e</em> flag along with the <em>resolution=value</em>
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+parameter, the region extents are based on new dataset. It is therefore
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+recommended to first use the <em>-s</em> flag to get the extents of the
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+LiDAR point cloud to be imported, then adjust the current region or the
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+<em>resolution</em> parameter accordingly, and only then proceed with
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+the actual import.
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<p>
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-<em>r.in.lidar</em> is designed for processing massive point cloud datasets,
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-for example raw LiDAR or sidescan sonar swath data. It has been tested with
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-datasets as large as tens of billion of points (705GB in a single file).
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- <!-- Doug Newcomb, US Fish & Wildlife Service -->
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+<em>r.in.lidar</em> is designed for processing massive point cloud
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+datasets, for example raw LiDAR or sidescan sonar swath data. It has
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+been tested with large datasets (see below for memory management
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+notes).
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<p>
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-Available statistics for populating the raster are:<br>
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+Available statistics for populating the output raster map are:<br>
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<ul>
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<li>
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<table>
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@@ -46,30 +50,25 @@ Available statistics for populating the raster are:<br>
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<h2>NOTES</h2>
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-<h3>Gridded data</h3>
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-
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-If data is known to be on a regular grid <em>r.in.lidar</em> can reconstruct
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-the map perfectly as long as some care is taken to set up the region
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-correctly and that the data's native map projection is used. A typical
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-method would involve determining the grid resolution either by examining
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-the data's associated documentation or by studying the text file. Next scan
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-the data with <em>r.in.lidar</em>'s <b>-s</b> (or <b>-g</b>) flag to find the
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-input data's bounds. GRASS uses the cell-center raster convention where
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-data points fall within the center of a cell, as opposed to the grid-node
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-convention. Therefore you will need to grow the region out by half a cell
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-in all directions beyond what the scan found in the file. After the region
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-bounds and resolution are set correctly with <em>g.region</em>, run
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-<em>r.in.lidar</em> using the <i>n</i> method and verify that n=1 at all places.
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-<em>r.univar</em> can help. Once you are confident that the region exactly
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-matches the data proceed to run <em>r.in.lidar</em> using one of the <i>mean,
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-min, max</i>, or <i>median</i> methods. With n=1 throughout, the result
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-should be identical regardless of which of those methods are used.
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+<h3>LAS file import preparations</h3>
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+
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+Since the <em>r.in.lidar</em> generates a raster map through binning
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+from the original LiDAR points, the target computational region
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+extent and resolution have to be determined. A typical workflow
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+would involve the examination of the LAS data's associated
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+documentation or the scan of the LAS data file with
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+<em>r.in.lidar</em>'s <b>-s</b> (or <b>-g</b>) flag to find the input
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+data's bounds.<br>
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+Another option is to automatically set the region extents based on the
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+LAS dataset (<b>-e</b> flag) along with the target raster resolution using
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+the <em>resolution</em> parameter.
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<h3>Memory use</h3>
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While the <b>input</b> file can be arbitrarily large, <em>r.in.lidar</em>
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-will use a large amount of system memory for large raster regions (10000x10000).
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+will use a large amount of system memory (RAM) for large raster regions
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+(> 10000x10000 pixels).
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If the module refuses to start complaining that there isn't enough memory,
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use the <b>percent</b> parameter to run the module in several passes.
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In addition using a less precise map format (<tt>CELL</tt> [integer] or
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