How does the lascatalog function in LIDR work?

How does the lascatalog function in LIDR work?

It expects a templated filename so each written file will be attributed a unique name. In the following example, several LAS files will be written to disk with names like 339348.8_5239254_1.las (center coordinates from each file) and the function returns a LAScatalog object that references all the new files instead of a list of LAS object.

How does point cloud indexation work in LIDR?

Point cloud indexation is a topic covered by this vignette. In short, LAS file indexation allows for faster queries when extracting ROIs in files. Under the hood lidR relies on LASlib to read files and inherits of the capability to leverage the use of LAX files developed by Martin Isenburg.

What do you need to know about LIDR package?

The lidR package provides a powerful and versatile set of tools to work with collections of files and enables the application of workflow with an automatic management of tile buffering, on disk storage, parallelization of tasks and so on.

How does LIDR use laslib to read files?

Under the hood lidR relies on LASlib to read files and inherits of the capability to leverage the use of LAX files developed by Martin Isenburg. While extracting hundreds of plots from hundreds of files may take many seconds, the use of index files can reduce processing to few seconds.

What is the output of lascatalog processing engine?

The output is a LAS object. Multiple extractions is also possible and is performed the same way by searching the corresponding files and then querying in each file no matter if the region of interest is situated in one or several files. The output becomes list of LAS objects.

What is the Las check function in lascatalog?

The las_check () function performs an inspection of LAScatalog objects for file consistency. For a deep (and longer) inspection of each file use deep = TRUE. This will sequentially load each file entirely.

Why do I need lascatalog for plot inventories?

The extraction of regions of interest (ROIs) such as plot inventories becomes difficult in these situations because one must search in which file and sometimes which file s the ROI belongs. It also makes the creation of continuous outputs such as a DTM, a raster of metrics, individual tree detection, or anything else far more complicated.

What is the purpose of the lascatalog class?

The LAScatalog class and the LAScatalog engine are intricately documented in two dedicated vignettes available here and here. The purpose of this book is to propose alternative documentation with more illustrated examples and real use cases. The function readLAScatalog () reads the header of all the LAS files of a given folder.

What is the clip ( ) function in lascataglog?

Functions using the clip_* () moniker are a good starting point for exploring the capabilities of the LAScataglog engine. clip_* () functions allow for the extraction of a region of interest (ROI) from a point cloud. The following example extracts a circle from a point cloud loaded into memory in a LAS object:

How to check the name of a LAS file?

In the following example, several LAS files will be written to disk with names like 339348.8_5239254_1.las (center coordinates from each file) and the function returns a LAScatalog object that references all the new files instead of a list of LAS object. We can check the files that were written on disk and see that the names match the template.

How to read.las or.laz files at once?

Reads .las or .laz files into an object of class LAS. If several files are read at once the returned LAS object is considered as one LAS file. The optional parameters enable the user to save a substantial amount of memory by choosing to load only the attributes or points of interest.

How to read a LAS file in LIDR?

If the desired filters are known before reading the file, the internal filters should always be preferred. The available filters are those from LASlib and can be found by running the following command: readLAS (filter = “-help”) . (see also rlas::read.las).

What should The Legend of a raster be?

The legend is continuous – with a range between 0 and 3. However you want to plot the data using discrete bins. Let’s clean up your plot legend. Given you have discrete values you will create a CUSTOM legend with the 3 categories that you created in your classification matrix.

How to classify a raster dataset in R?

SECTION 12 FALL BREAK: NO CLASS! Lesson 5. Classify a Raster in R. Reclassify a raster dataset in R using a set of defined values. Describe the difference between using breaks to plot a raster compared to reclassifying a raster object.