" ability to take a point cloud that has no color information on it, and overlay it into an orthophoto to produce a colorized point cloud"
Add-on to ArcGIS
LP360 for ArcGIS™ (Basic, Standard and Advanced)
I'm a little late to the party but here is another suggestion: http://potree.org/
It's an open souce, WebGL based point cloud viewer I've been working on for quite a while.
== UPDATE ==
It can render large amounts of colored point clouds. LIDAR data without colors will be supported soon.
Source code: https://github....
According to Diego Alonso's comments from the mappingGIS blog1, this error is related to QGIS version 2.14. With the upgrade, the standalone installer eliminated the msys folder from GRASS 7 algorithms.
To bypass this error, go to Processing -> Options -> Provider and deactivate all GRASS folders from previous versions. Set these paths as blanks. Then, ...
ESRI has a pretty good help section on LiDAR (below). For more formal details on LiDAR, I would recommend the following books:
Topographic Laser Ranging and Scanning: Principles and
Airborne and Terrestrial Laser Scanning
Remote Sensing and Image Interpretation
LiDAR Laser Returns
Laser pulses emitted from a lidar system reflect from ...
I decided to merge other answers with mine and organize them into a tabular format. I think it is easier to read and manage for future visitors:
The table can be accessed from the following link in csv format:
View in tabular form: Free LiDar DataSources
Download (csv): Free LiDar DataSources
Please submit pull request if you intended to add to this ...
lidR is a great package in R for forestry applications.
From the GitHub lidR documentation:
R package for Airborne LiDAR Data Manipulation and Visualization for
The lidR package provides functions to read and write .las and .laz
files, plot point clouds, compute metrics using an area-based
approach, compute digital ...
Generating LiDAR DEMs from unclassified point clouds with:
MCC-LIDAR - Multiscale Curvature Classification (MCC) algorithm.
(supports LAS versions 1.1 to 1.3)
MCC-LIDAR is a command-line tool for processing discrete-return LIDAR data in forested environments (Evans & Hudak, 2007).
a) unclassified point cloud.
b) ground returns ...
ArcGis has a new LiDAR dataset at 10.1 which will allow you to view your LiDAR data directly and also see more information than what's in a raster... for example you can add to ArcMap and then filter down the display to just tree classes, or just first returns!
LiDAR data contains much more than just elevation, there's intensity that is stored by default (...
Your PointsXYZIC is now a numpy array. Which means you can use numpy indexing to filter the data you're interested in. For example you can use an index of booleans to determine which points to grab.
#the values we're classifying against
unclassified = 1
ground = 2
#create an array of booleans
filter_array = np.any(
PointsXYZIC[:, 4] == ...
When converting a LiDAR dataset to a DEM, you are taking a set of discrete data points and converting them into a single, continuous dataset. Let's say that your .las file contains X (latitude), Y (longitude) and Z (elevation) values with an average resolution of ~1 meters. The resolution here is really important- we're only talking about an average and so ...
laspy is another good LAS read/write software. It supports working with the data directly in numpy arrays and a number of other nice Pythonic features. It isn't processing software per se, however.
PDAL has the ability to use Python as an in-pipeline filtering language, but this isn't a processing engine either.
There isn't too much in the Python quiver ...
For converting to las 1.2 from las 1.4, PDAL's translate command is an option:
pdal translate --writers.las.minor_version=2 input-las14.las output-las12.las
LAStools can also do the job:
las2las -i input-las14.las -set_version 1.2 -o output-las12.las
In general, las 1.4 support is patchy among free and open-source las-aware software, e.g. liblas doesn't ...
It sounds like you want either a voxel-based thinning or maybe a Poisson-based one. PDAL can do either. See PDAL's tutorial on the topic at https://pdal.io/tutorial/sampling/index.html
As far as the size of the file, please define "large". Just about any technique except simple rank decimation (remove n-th points) is going to want to have access to the ...
Have you consider to use GRASS GIS analysis? I have expirience that GRASS algorithms have very good accurance on hydrology analysis. For example, I want to generate something like drainage network on DTM with resolution 5x5m. I had compared tools from ArcMap (including ArcHydro Tools) and you can view the result on first picture (red lines). Then I tried to ...
Set the scaling and offset when reprojecting to WGS84, e.g.:
las2las --a_srs EPSG:26911 --t_srs EPSG:4326 -i file1.las -o output.las --scaling 1e-7 1e-7 0.01 --offset <something close to your data's longitudes>,<something close to your data's latitudes>,0
You've been caught by a limitation/feature of the las file format. Internally,...
Thank you for clarifying your question as it was previously quite unclear. You can read a multiband raster using the stack or brick function in the raster package and assign the associated RGB values to an sp SpatialPointsDataFrame object using extract, also from raster. Coercion of the data.frame object (which results from read.csv) to an sp point object,...
A common mistake (that I've made too) is to down-sample a raster using the resample tool with bilinear interpolation. See this answer for an explanation why this is not good. A raster can be down-sampled in three steps.
The first step might not be required. Reproject the raster to the target extents. Use bilinear interpolation, and keep the output cell size ...
Inside a Geodatabase, there are certain ways you can't name a table or Feature Class. Beginning with a number is one of those ways.
Validating Table and Field Names in Python:
Try renaming your table to las_1 or something and see if it works
If you are open to using alternative software to solve your problem, then I can suggest the Remove Off-Terrain Objects tool of the cross-platform open-source GIS Whitebox Geospatial Analysis Tools (of which I am lead developer). I realize that you said in your question that you could not convert your data to LAS format, but the tool takes a raster, not LAS ...
There is a considerable body of literature on individual crown detection in spectral and lidar data. Methods wise, perhaps start with:
Falkowski, M.J., A.M.S. Smith, P.E. Gessler, A.T. Hudak, L.A. Vierling and J.S. Evans. (2008). The influence of conifer forest canopy cover on the accuracy of two individual tree measurement algorithms using lidar data. ...
The picture below from Fernandez-Diaz (2011) might help complementing Aaron's answer.
Lidar returns are discrete observations* recorded when a laser pulse is intercepted and reflected by targets. Multiple returns derive from one laser pulse intercepting multiple targets (e.g. a top of a tree, its branches, and the ground).
*such as coordinates x, y and z; ...
If you have the opportunity to get LAS or LAZ instead of TXT ... absolutely go for it. It will be trivial for you to go from LAS or LAZ to TXT (for example with the free and open source las2txt tool from LAStools). Ordering TXT instead of LAS means to loose many important attributes that you may not care about today but will in a few months or years. It also ...
The current option to import las files in ArcGIS is the LAS dataset data type. According to ESRI:
The LAS dataset provides fast access to large volumes of lidar and surface data without the need for data conversion.
If the surface (raster) you are trying to create is a bare-earth DEM refer to the following posts:
One option is the LAS dataset to Raster ...
I read about variety of algorithms for the job (ie. as per @Hornbydds link).
I tried couple appoches, and the best results in my case yield Standard Terrain Analysis from SAGA. Here is what I did and why:
Dikes are usually highest feature in the vicinity of river channel, so I turned them into channels by flipping DEM (MapAlgebra DEM * -1 or for ...
I've used SAGA-GIS for identifying tree canopy and creating DSM's from Lidar data. I was very impressed.
SAGA seems to be an all around Vector/Rastor/Point Cloud processing tool. It is free and open source. It comes as 32-bit or 64-bit. It does have some scripting capabilities if you build the source code yourself with Python Bindings, but all the tools ...
libLAS can indeed be used commercially. So can Martin Isenburg's LASlib, which is LGPL, and speaking as the author of libLAS, faster and more completely supported than libLAS. Both are indeed C++ libraries, however, and there isn't too much in the ASPRS LAS space for native .NET.
I'm also the primary author of PDAL, and PDAL can also read ASPRS LAS data, ...
FUSION LiDAR Toolkit has clipping capabilities (PolyClipData tool) and unlike LASTools, its usage is unrestricted. However, despite the fact that some SVN repository on SourceForge exists, the source code published is incomplete and very old. If you can proceed without knowing the code and just use the compiled binary, then FUSION should be fine for this ...
Martin is correct that while your workflow will do well for a specific user case, it doesn't account for many of the issues that road embankments create for flowpath modelling using fine-resolution LiDAR data, such as the problems with discontinuous flow in roadside ditches and the effects of minor unmapped culverts (which can alter flowpaths considerably). ...