Prototype for “native LAS 1.4 extension” of LASzip LiDAR Compressor Released

PRESS RELEASE (for immediate release)
February 13, 2017
rapidlasso GmbH, Gilching, Germany

Just in time for ILMF 2017 in Denver, the makers of the popular LiDAR processing software LAStools announce that the prototype for the “native LAS 1.4 extension” of their award-winning open source LASzip LiDAR compressor is ready for testing. An update to the compressed LAZ format had become necessary due to a core change in the ASPRS LAS 1.4 specification which had introduced several new point types.

A new feature of the updated LASzip compressor is the ability to selectively decompress of only those attributes of each point that really are needed by the application that is reading the LAZ file. Minimally this will be the x and y coordinate of each point and the return counts, which are sufficient to – for example – calculate the exact extend of the survey area. Most applications will also want to access z coordinate. However, the intensities, the GPS times, the RGB or NIR colors, and the new “Extra Bytes” are often not needed. As the updated LAZ format compresses these different attributes into separate layers, their decompression can then be skipped. Therefore sometimes only 40% of a compressed LAZ file needs to be decompressed to access the coordinates of points with many attributes.

percentage of bytes in a compressing LAZ file corresponding to different point attributes

The percentages of a compressed LAZ file used to encode different point attributes for two example LAS 1.4 files.

The new LASzip prototype is currently being crowd-tested. Interested parties who already have holdings of LAS 1.4 files with point types 6 to 10 may send an email to ‘’ to participate in these tests.

The release of the new LASzip compressor comes more than a year late as development had been intentionally delayed to give ESRI an opportunity to contribute their needs and ideas to create a joint open format with the community and avoid LiDAR format fragmentation. Sadly, this effort ultimately failed.

About rapidlasso GmbH:
Technology powerhouse rapidlasso GmbH specializes in efficient LiDAR processing tools that are widely known for their high productivity. They combine robust algorithms with efficient I/O and clever memory management to achieve high throughput for data sets containing billions of points. The company’s flagship product – the LAStools software suite – has deep market penetration and is heavily used in industry, government agencies, research labs, and educational institutions. Visit for more information.

Pre-Processing Mobile Rail LiDAR with LAStools

The majority of LAStools users are processing airborne LiDAR. That should not surprise as airborne is by far the most common form of LiDAR in terms of square kilometers covered. The availability of LiDAR as “open data” is also pretty much restricted to airborne surveys, which are often tax-payer funded and then distributed freely to achieve maximum return of investment.

But folks are increasingly using our software to do some of the “heavy lifting” for mobile LiDAR, either mounted on a truck for scanning cities or on a train for capturing railroad infrastructure. The LiDAR collected for the cities of Budapest and Singapore, for example, was pre-processed by multi-core scripted LAStools when the scanning trucks returned with their daily trajectories worth of point clouds captured by a RIEGL VMX-450 mobile mapping system.

One customer who was recently scanning railroad infrastructure wanted to do automatic ground classification as a first step prior to further segmentation of the data. We were asked for advice because on such data the standard settings of lasground left too many patches of ground unclassified. Also the uniform tiling lastile generates by default is not a good way to break such data into manageable pieces given the drastically varying point densities in mobile scanning.

We obtained a 217 MB file in LAZ format with 40 million points corresponding to a 2.7 km stretch of railway track. We first run a quick lasindex (with the options for ‘mobile’) on the file that creates a spatial indexing LAX file with maximally 10 meter resolution. This not only allows faster area-of-interest queries but also gives us a more detailed preview than just the bounding box of where the LiDAR points actually are in the GUI of LAStools.


Presence of LAX files results in actual extend of LiDAR being shown in GUI.

lasindex -i segment.laz -tile_size 10 -maximum -100

We then run lastile four times to create an adaptive tiling in which no tile has more than 6 million points. The first call creates the initial 1000 by 1000 meter tiles. The following three calls refine all those tiles that still have more than 6 million points first into 500 by 500 meter, then 250 by 250 meter, and finally 125 by 125 meter tiles in parallel on 4 cores. Note the ‘-refine_tiling’ option is used in the first call to lastile and the ‘-refine_tiles’ option in all subsequent calls.

lastile -i segment.laz ^
        -tile_size 1000 ^
        -buffer 10 -flag_as_withheld ^
        -refine_tiling 6000000 ^
        -odir tiles_raw -o rail.laz
lastile -i tiles_raw\*_1000.laz ^
        -flag_as_withheld ^
        -refine_tiles 6000000 ^
        -olaz ^
        -cores 4
lastile -i tiles_raw\*_500.laz ^
        -flag_as_withheld ^
        -refine_tiles 6000000 ^
        -olaz ^
        -cores 4
lastile -i tiles_raw\*_250.laz ^
        -flag_as_withheld ^
        -refine_tiles 6000000 ^
        -olaz ^
        -cores 4

The resulting tiles all have fewer than 6 million points but still have the initial 10 meter buffer that was specified by the first call to lastile. Two tiles were sufficiently small after the 1st call, three tiles after the 2nd call, eleven tiles after 3rd call, and three tiles after the 4th.

contents of tile shown in blue in adaptive tiling below

points of adaptive tile (high-lighted in blue below) colored by intensity

Adaptive tiling created with four calls to lastile.

Adaptive tiling created with four calls to lastile. Scale factors of 0.00025 (see mouse cursor) implies that point coordinates are stored with quarter millimeter resolution. Lowering them to 0.001 would result in better compression and lower I/O.

Noise in the data – especially low noise – can lead lasground into choosing the wrong points during ground classification by latching on to those low noise points. We first classify the noise points into a different class (7) using lasnoise so we can later ignore them. These particular settings were found by experimenting on a few tiles with different values (see the README file) until visual inspection showed that most low points had been classified as noise.

lasnoise -i tiles_raw\*.laz ^
         -step_xy 0.5 -step_z 0.1 ^
         -odir tiles_denoised -olaz ^
         -cores 4
noise points shown in violett

noise points shown in violett

The points classified as noise will not be considered as ground points during the next step. For this it matters little that lamp posts, wires, or vegetation are wrongly marked as noise now. We can always undo their noise classification once the ground points were classified. Important is that those pointed to by the mouse cursor, which are below the desired ground, are excluded from consideration during the ground classification step. Here those low points are not actually noise but returns generated wherever the laser was able to “peek” through an opening to a lower surface.

lasground -i tiles_denoised\*.laz ^
          -ignore_class 7 ^
          -step 1 -sub 3 -bulge 0.1 -spike 0.1 -offset 0.02 ^
          -odir tiles_ground -olaz ^
          -cores 4

For classification with lasground there are a number of options to play with  (see the README file) but the most important is the correct step size. It is terrain along the railway track bed that is supposed to get represented well. The usual step of 5 to 40 meter for lasground aim at the removal of vegetation and man-made structures from airborne LiDAR. They are not the right choice here. A step of 1 and the parameters shown above gives us the ground shown below.

Classification of terrain along railway track using lasground with '-step 1'

Classification of terrain along railway track bed using lasground with ‘-step 1’

The new ‘-flag_as_withheld’ option in lastile that flags each point in the buffer with the withheld flag is useful in case we want to remove all buffer points on-the-fly, for example, in order to create a DTM hillshade of 25 cm resolution for a visual quality check of the entire 2.7 km track using blast2dem from the BLAST extension of LAStools.

blast2dem -i tiles_ground\*.laz -merged ^
          -drop_withheld -keep_class 2 ^
          -hillshade -step 0.25 ^
          -o dtm_hillshaded.png
Small 600 x 600 pixel detail of hill-shaded 5663 x 9619 pixel DTM raster generated by blast2dem

Small 600 x 600 pixel detail of hill-shaded 5663 x 9619 pixel DTM raster generated by blast2dem.

NRW Open LiDAR: Download, Compression, Viewing

This is the first part of a series on how to process the newly released open LiDAR data for the entire state of North Rhine-Westphalia that was announced a few days ago. Again, kudos to OpenNRW for being the most progressive open data state in Germany. You can follow this tutorial after downloading the latest version of LAStools as well as a pair of DGM and DOM files for your area of interest from these two download pages.

We have downloaded the pair of DGM and DOM files for the Federal City of Bonn. Bonn is the former capital of Germany and was host to the FOSS4G 2016 conference. As both files are larger than 10 GB, we use the wget command line tool with option ‘-c’ that will restart where it left off in case the transmission gets interrupted.

The DGM file and the DOM file are zipped archives that contain the points in 1km by 1km tiles stored as x, y, z coordinates in ETRS89 / UTM 32 projection as simple ASCII text with centimeter resolution (i.e. two decimal digits).

>> more
360000.00 5613026.69 164.35
360000.00 5613057.67 164.20
360000.00 5613097.19 164.22
360000.00 5613117.89 164.08
360000.00 5613145.35 164.03

There is more than one tile for each square kilometer as the LiDAR points have been split into different files based on their classification and their return type. Furthermore there are also synthetic points that were used by the land survey department to replace certain LiDAR points in order to generate higher quality DTM and DSM raster products.

The zipped DGM archive is 10.5 GB in size and contains 956 *.xyz files totaling 43.5 GB after decompression. The zipped DOM archive is 11.5 GB in size and contains 244 *.xyz files totaling 47.8 GB. Repeatedly loading these 90 GB of text data and parsing these human-readable x, y, and z coordinates is inefficient with common LiDAR software. In the first step we convert the textual *.xyz files into binary *.laz files that can be stored, read and copied more efficiently. We do this with the open source LASzip compressor that is distributed with LAStools using these two command line calls:

laszip -i dgm1l_05314000_Bonn_EPSG5555_XYZ\*.xyz ^
       -epsg 25832 -vertical_dhhn92 ^
       -olaz ^
       -cores 2
laszip -i dom1l_05314000_Bonn_EPSG5555_XYZ\*.xyz ^
       -epsg 25832 -vertical_dhhn92 ^
       -olaz ^
       -cores 2

The point coordinates are is in EPSG 5555, which is a compound datum of horizontal EPSG 25832 aka ETRS89 / UTM zone 32N and vertical EPSG 5783 aka the “Deutsches Haupthoehennetz 1992” or DHHN92. We add this information to each *.laz file during the LASzip compression process with the command line options ‘-epsg 25832’ and ‘-vertical_dhhn92’.

LASzip reduces the file size by a factor of 10. The 956 *.laz DGM files compress down to 4.3 GB from 43.5 GB for the original *.xyz files and the 244 *.laz DOM files compress down to 4.8 GB from 47.8 GB. From here on out we continue to work with the 9 GB of slim *.laz files. But before we delete the 90 GB of bulky *.xyz files we make sure that there are no file corruptions (e.g. disk full, truncated files, interrupted processes, bit flips, …) in the *.laz files.

laszip -i dgm1l_05314000_Bonn_EPSG5555_XYZ\*.laz -check
laszip -i dom1l_05314000_Bonn_EPSG5555_XYZ\*.laz -check

One advantage of having the LiDAR in an industry standard such as the LAS format (or its lossless compressed twin, the LAZ format) is that the header of the file stores the number of points per file, the bounding box, as well as the projection information that we have added. This allows us to very quickly load an overview for example, into lasview.

lasview -i dgm1l_05314000_Bonn_EPSG5555_XYZ\*.laz -GUI
The bounding boxes of the DGM files quickly display a preview of the data in the GUI when the files are in LAS or LAZ format.

The bounding boxes of the DGM files quickly give us an overview in the GUI when the files are in LAS or LAZ format.

Now we want to find a particular site in Bonn such as the World Conference Center Bonn where FOSS4G 2016 was held. Which tile is it in? We need some geospatial context to find it, for example, by creating an overview in form of KML files that we can load into Google Earth. We use the files from the DOM folder with “fp” in the name as points on buildings are mostly “first returns”. See what our previous blog post writes about the different file names if you can not wait for the second part of this series. We can create the KML files with lasboundary either via the GUI or in the command line.

lasboundary -i dom1l_05314000_Bonn_EPSG5555_XYZ\dom1l-fp*.laz ^
Only the "fp" tiles from the DOM folder loaded the GUI into lasboundary.

Only the “fp” tiles from the DOM folder loaded the GUI into lasboundary.

lasboundary -i dom1l_05314000_Bonn_EPSG5555_XYZ\dom1l-fp*.laz ^
            -use_bb -labels -okml

We zoom in and find the World Conference Center Bonn and load the identified tile into lasview. Well, we did not expect this to happen, but what we see below will make this series of tutorials even more worthwhile. There is a lot of “high noise” in the particular tile we picked. We should have noticed the unusually high z range of 406.42 meters in the Google Earth pop-up. Is this high electromagnetic radiation interfering with the sensors? There are a number of high-tech government buildings with all kind of antennas nearby (such as the United Nations Bonn Campus the mouse cursor points at).

Significant amounts of high noise are in the first returns of the DOM tile we picked.

Significant amounts of high noise are in the first returns of the DOM tile we picked.

But the intended area of interest was found. You can see the iconic “triangulated” roof of the building that is across from the World Conference Center Bonn.

The World Conference Center Bonn is across from the building with the "triangulated" roof.

The World Conference Center Bonn is across from the building with the “triangulated” roof.

Please don’t think it is the responsibility of OpenNRW to remove the noise and provide cleaner data. The land survey has already processed this data into whatever products they needed and that is where their job ended. Any additional services – other than sharing the raw data – are not in their job description. We’ll take care of that … (-:

Acknowledgement: The LiDAR data of OpenNRW comes with a very permissible license. It is called “Datenlizenz Deutschland – Namensnennung – Version 2.0” or “dl-de/by-2-0” and allows data and derivative sharing as well as commercial use. It only requires us to name the source. We need to cite the “Land NRW (2017)” with the year of the download in brackets and specify the Universal Resource Identification (URI) for both the DOM and the DGM. Done. So easy. Thank you, OpenNRW … (-:

Creating a Better DTM from Photogrammetic Points by Avoiding Shadows

At INTERGEO 2016 in Hamburg, the guys from Aerowest GmbH shared with us a small photogrammetric point cloud from the city of Soest that had been generated with the SURE dense-matching software from nFrames. We want to test whether – using LAStools – we can generate a decent DTM from these points that are essentially a gridded DSM. If this interest you also see this, this, this, and this story.


Here you can download the four original tiles (tile1, tile2, tile3, tile4) that we are using in these experiments. We first re-tile them into smaller 100 meter by 100 meter tiles with a 20 meter buffer using lastile. We use option ‘-flag_as_withheld’ that flags all the points falling into the buffer using the withheld flag so they can easily be removed on-the-fly later with the ‘-drop_withheld’ filter (see the README for more on this). We also add the missing projection with ‘-epsg 32632’.

lastile -i original\*.laz ^
        -tile_size 100 -buffer 20 ^
        -flag_as_withheld ^
        -epsg 32632 ^
        -odir tiles_raw -o soest.laz

Below is a screenshot from one of the resulting 100 meter by 100 meter tiles (with 20 meter buffer) that we will be focusing on in the following experiments.

The tiles 'soest_437900_5713800.laz'

The tile ‘soest_437900_5713800.laz’ used in all experiments.

Next we use lassort to reorder the points ordered along a coherent space-filling curve as the existing scan-line order has the potential to cause our triangulation engine to slow down. We do this on 4 cores.

lassort -i tiles_raw\*.laz ^
        -odir tiles_sorted -olaz ^
        -cores 4

We then use lasthin to lower the number of points that we consider as ground points (see the README for more info on this tool). We do this because the original 5 cm spacing of the dense matching points is a bit excessive for generating a DTM with a resolution of, for example, 50 cm. Instead we only use the lowest point in each 20 cm by 20 cm cell as a candidate for becoming a ground point, which reduces the number of considered points by a factor of 16. We achieve this by classifying these lowest point to a unique classification code (here: 9) and later tell lasground to ignore all other classifications.

lasthin -i tiles_sorted\*.laz ^
        -step 0.2 -lowest -classify_as 9 ^
        -odir tiles_thinned -olaz ^
        -cores 4
Then we run lasground on 4 cores to classify the ground points with options ‘-step 20’, ‘-bulge 0.5’, ‘-spike 0.5’ and ‘-fine’ that we selected after two trials on a single tile. There are several other options in lasground to play with that may achieve better results on other data sets (see README file for more on this). The ‘-ignore_class 0’ option instructs lasground to ignore all points that are not classified so that only those points that lasthin was classifying as 9 in the previous step are considered.
lasground -i tiles_thinned\*.laz ^
          -step 20 -bulge 0.5 -spike 0.5 -fine ^
          -ignore_class 0 ^
          -odir tiles_ground -olaz ^
          -cores 4
However, when we scrutinize the resulting ground classification notice that there are bumps in the corresponding ground TIN that seem to correspond to areas where the original imagery has dark shadows that in turn seem to significantly affect the geometric accuracy of the points generated by the dense-matching algorithm.
Looking a the bump from below we identify the RGB colors of the points have that form the bump that seem to be of much lower accuracy than the rest. This is an effect that we have noticed in the past for data generated with other dense-matching software and maybe an approach similar to the one we take here could also help with this low noise. It seems that points that are generated from shadowed areas in the input images can have a lot lower accuracy than those from well-lit areas. We use this correlation between RGB color and geometric accuracy to simply exclude all points whose RGB colors indicate that they might be from shadow areas from becoming ground points.
The RGB colors of low-accuracy points are mostly from very dark shadowed areas.

The RGB colors of low-accuracy points are mostly from very dark shadowed areas.

We use las2las with the relatively new ‘-filtered_transform’ option to reclassify all points whose RGB color is close to zero to yet classification code 7 (see README file for more on this). We do this for all points whose red value is between 0 and 30, whose green value is between 0 and 35, and whose blue value is between 0 and 40. Because the RGB values were stored with 16 bits in these files we have to multiply these values with 256 when applying the filter.
las2las -i tiles_thinned\*.laz ^
        -keep_RGB_red 0 7680 ^
        -keep_RGB_green 0 8960 ^
        -keep_RGB_blue 0 10240 ^
        -filtered_transform ^
        -set_classification 7 ^
        -odir tiles_rgb_filtered -olaz ^
        -cores 4
Below you see all points that will no longer be considered because their classification was set to 7 by the command above.
Points whose RGB values indicate they might lie in the shadows.

Points whose RGB values indicate they might lie in the shadows.

We then re-run lasground with the same options ‘-step 20’, ‘-bulge 0.5’, ‘-spike 0.5’ and ‘-fine’ as before but now we ignore all points that are still have classification 0 because they were not classified as 9 by lasthin earlier and we also ignore all points that have been assigned classification 7 by las2las in the previous step.
lasground -i tiles_thinned\*.laz ^
          -step 20 -bulge 0.5 -spike 0.5 -fine ^
          -ignore_class 0 7 ^
          -odir tiles_ground -olaz ^
          -cores 4
The situation has significantly improved for the bumb we saw before as you can see in the images below.

Finally we create a DTM with blast2dem (see README) and a DSM with lasgrid (see README) by merging all points into one file but dropping the buffer points that were marked as withheld by the initial run of lastile (see README).

blast2dem -i tiles_ground\*.laz -merged ^
          -drop_withheld -keep_class 2 ^
          -step 0.5 ^
          -o dtm.bil

lasgrid -i tiles_ground\*.laz -merged ^
        -drop_withheld ^
        -step 0.5 -average ^
        -o dsm.bil
 As our venerable lasview (see README) can directly read BIL rasters as points (just like all the other LAStools), so we can compare the DTM and the DTM by loading them as two flight lines (‘-faf’) and then switch back and forth between the two by pressing ‘0’ and ‘1’ in the viewer.
lasview -i dtm.bil dsm.bil -faf

Above you see the final DTM and the original DSM. So yes, LAStools can definitely create a DTM from point clouds that are the result of dense-matching photogrammetry. We used the correlation between shadowed areas in the source image and geometric errors to remove those points from consideration for ground points that are likely to be too low and result in bumps. For dense-matching algorithms that also output an uncertainty value for each point there is the potential for even better results as our range of eliminated RGB colors may not cover all geometrically uncertain points while at the same time may be too conservative and also remove correct ground points.

Fixing Intensity Differences between Flightlines (“quick and dirty”)

Visiting our users on-site, such as last week at Mariano Marcos State University in Ilocos Norte in the Philippines, we sometimes come across situations as pictured below where the intensity values of the returns of one flightline are drastically different from that of other flightlines.

The intensity of returns in the left most flightline is different from that of other flightlines.

The intensity of returns in the left most flightline is different from that of other flightlines.

Using intensity rasters with such dark strips as an additional input for land cover classification may likely make things worse. Radiometrically “correct” intensity calibration is a complex topic and may not always be possible to do using only the LAZ files without meta information such as the internals of the scanning system and the aircraft trajectory. However, we now describe a “quick and dirty” fix to the situation shown above so that the intensity grids (that were computed as averages of first return intensities) at least “look” as sensible as for the one square tile (shown below) that was corrected by a simple multiplication with 5 for all intensities of the dark strip.

Simply multiplying all intensities of the dark flightline with 5 seems to "fix" the issue.

Simply multiplying all intensities of the dark flightline with 5 seems to “fix” the issue for our test tile.

The number 5 was determined by a quick glance at the intensity histograms that we can generate with lasinfo. We decide to only look at single returns as we expect them to have a higher correlation: Their locations are more likely to be “seen similarly” from and their energy is more likely “reflected similarly” to different flightlines compared to that of multiple returns.

lasinfo -i strip1.laz strip2.laz strip3.laz ^
        -keep_single ^
        -histo intensity 1 ^
        -nmm -nh -nv ^
        -odix _histo_int -otxt

The resulting histograms for the dark ‘strip1.laz’ is quite different from that of the much brighter ‘strip2.laz’ and ‘strip3.laz’. The average single return intensity for the dark ‘strip1.laz’ is a meager 5.13 whereas the  brighter ‘strip2.laz’ and ‘strip3.laz’ have similar averages of 24.15 and 24.50 respectively.

Draw your own conclusion about which scale factor to use. We have the choice to match either the peak of the histograms or their averages. Scaling the peak of 3 for ‘strip1.laz’ to match the 25 of the other two strips is too much of an upscaling. But the average 24.15 divided by 5.13 gives a potential scale of 4.71 and the average 24.50 divided by 5.13 gives a potential scale of 4.77 and we already saw a multiplication by 5 giving reasonable results. So this is how we can fix the intensity:

las2las -i strip1.laz ^
        -scale_intensity 4.75 ^
        -odix _corr_int -olaz

But what if your data is already in tiles? How can you adjust only the intensity of those returns that are from the flightline 1? Assuming that your flightline information is properly stored in the point source ID field of every point this is easily done with the new ‘-filtered_transform’ in LAStools using las2las on as many cores as you have as follows:

las2las -i tiles/*.laz ^
        -keep_point_source 1 ^
        -filtered_transform ^
        -scale_intensity 4.75 ^
        -odir tiles_corr -olaz ^
        -cores 8

This is not currently exposed in the GUI of las2las but you can simply add it by typing it into the ‘RUN’ pop-up window as shown below.

Scaling only the intensities of flightline 1 by 4.9 using the new '-filtered_transform'.

Scaling only the intensities of flightline 1 by 4.9 using the new ‘-filtered_transform’.

After this “quick and dirty” intensity correction we again ran lasgrid as follows:

lasgrid -i tiles_corr/*.laz ^
        -gray -set_min_max 0 60 ^
        -odir tiles_int_rasters -opng ^
        -cores 8

And the result is shown below. The obvious flightline-induced discontinuity in the intensities has pretty much disappeared. Do you have similar flightline-related intensity issues? We like to hear from you whether this technique works or if we need to implement something more clever in the future …


New ‘laspublish’ creates Web Portals for 3D Viewing and Downloading of LiDAR

PRESS RELEASE (for immediate release)
February 18, 2016
rapidlasso GmbH, Gilching, Germany

Just in time for ILMF 2016, the makers of the open LASzip LiDAR compressor annouce the latest addition to their LiDAR processing software LAStools. The new ‘laspublish‘ from rapidlasso GmbH creates stand-alone Web Portals for interactive 3D viewing of LiDAR points and for selective downloading of LAZ or LAS files. The new tool is based on the cutting-edge streaming point cloud viewing technology of Potree that optimizes large LiDAR point clouds for streaming via the Web such that anyone can visualize, explore, and (optionally) download them with any modern browser.

3D viewer and download portal created with 'laspublish'

3D viewer and download portal created with ‘laspublish’

The interactive 3D viewer streams on demand only the relevant parts of the point clouds. It not only visualizes the LiDAR in many useful and intuitive ways, but is also equipped with measurement tools to calculate distances or areas and profile or clipping tools for close-up inspections. With its integrated LASzip compression, options to color LiDAR by classification, return type, intensity, and point source ID, stunning visuals via Eye Dome Lighting (EDL), and the optional 2D download map, the new ‘laspublish‘ empowers professional and novice users alike to create stand-alone LiDAR Webportals with just a few button clicks.

a few clicks and 'laspublish' creates a professional LiDAR portal

a few clicks and ‘laspublish’ creates a professional LiDAR portal

The new ‘laspublish‘ is now an integral part of the LAStools software and is bundled together with all necessary components of the Potree software. It provides an instant and cost-effective solution for generating a set of Web pages that realize a self-contained LiDAR portal offering interactive online visualization and exploration as well as easy and intuitive distribution of large LiDAR data sets via the optional download map.

About rapidlasso GmbH:
Technology powerhouse rapidlasso GmbH specializes in efficient LiDAR processing tools that are widely known for their high productivity. They combine robust algorithms with efficient I/O and clever memory management to achieve high throughput for data sets containing billions of points. The company’s flagship product – the LAStools software suite – has deep market penetration and is heavily used in industry, government agencies, research labs, and educational institutions. Visit for more information. As the only Diamond sponsor, rapidlasso GmbH has been the main financial supporter of the open source Potree package by Markus Schütz over the past two years.

About Potree:
Potree is a WebGL based viewer for large point clouds. The project evolved as a Web based viewer from the Scanopy desktop point cloud renderer by TU Wien, Institute of Computer Graphics and Algorithms. It will continue to be free and open source with a FreeBSD license to enable anyone to view, analyze and publicly share their large datasets. Visit for more information.

Generating Spike-Free Digital Surface Models from LiDAR

A Digital Surface Model (DSM) represents the elevation of the landscape including all vegetation and man-made objects. An easy way to generate a DSM raster from LiDAR is to use the highest elevation value from all points falling into each grid cell. However, this “binning” approach only works when then the resolution of the LiDAR is higher than the resolution of the raster. Only then sufficiently many LiDAR points fall into each raster cell to prevent “empty pixels” and “data pits” from forming. For example, given LiDAR with an average pulse spacing of 0.5 meters one can easily generate a 2.5 meter DSM raster with simple “binning”. But to generate a 0.5 meter DSM raster we need to use an “interpolation” method.

Returns of four fightlines on two trees.

Laser pulses and discrete returns of four fightlines.

For the past twenty or so years, GIS textbooks and LiDAR tutorials have recommened to use only the first returns to construct the interpolating surface for DSM generation. The intuition is that the first return is the highest return for an airborne survey where the laser beams come (more or less) from above. Hence, an interpolating surface of all first returns is constructed – usually based on a 2D Delaunay triangulation – and the resulting Triangular Irregular Network (TIN) is rasterized onto a grid at a user-specified resolution to create the DSM raster. The same way a Canopy Height Model (CHM) is generated except that elevations are height-normalized either before or after the rasterization step. However, using a first-return interpolation for DSM/CHM generation has two critical drawbacks:

(1) Using only first returns means not all LiDAR information is used and some detail is missing. This is particularly the case for off-nadir scan angles in traditional airborne surveys. It becomes more pronounced with new scanning systems such as UAV or hand-held LiDAR where laser beams no longer come “from above”. Furthermore, in the event of clouds or high noise the first returns are often removed and the remaining returns are not renumbered. Hence, any laser shot whose first return reflects from a cloud or a bird does not contribute its highest landscape hit to the DSM or CHM.

(2) Using all first returns practically guarantees the formation of needle-shaped triangles in vegetated areas and along building roofs that appear as spikes in the TIN. This is because at off-nadir scan angles first returns are often generated far below other first returns as shown in the illustration above. The resulting spikes turn into “data pits” in the corresponding raster that not only look ugly but impact the utility of the DSM or CHM in subsequent analysis, for example, in forestry applications when attempting to extract individual trees.

In the following we present results and command-line examples for the new “spike-free” algorithm by (Khosravipour et. al, 2015, 2016) that is implemented (as a slow prototype) in the current LAStools release. This completely novel method for DSM generation triangulates all relevant LiDAR returns using Contrained Delaunay algorithm. This constructs a “spike-free” TIN that is in turn rasterized into “pit-free” DSM or CHM. This work is both a generalization and an improvement of our previous result of pit-free CHM generation.

We now compare our “spike-free” DSM to a “first-return” DSM on the two small urban data sets “france.laz” and “zurich.laz” distributed with LAStools. Using lasinfo with options ‘-last_only’ and ‘-cd’ we determine that the average pulse spacing is around 0.33 meter for “france.laz” and 0.15 meter for “zurich.laz”. We decide to create a hillshaded 0.25 meter DSM for “france.laz” and a 0.15 meter DSM for “zurich.laz” with the command-lines shown below.

las2dem -i ..\data\france.laz ^
        -keep_first ^
        -step 0.25 ^
        -hillshade ^
        -o france_fr.png
las2dem -i ..\data\france.laz ^
        -spike_free 0.9 ^
        -step 0.25 ^
        -hillshade ^
        -o france_sf.png
las2dem -i ..\data\zurich.laz ^
           -keep_first ^
           -step 0.15 ^
           -hillshade ^
           -o zurich_fr.png
las2dem -i ..\data\zurich.laz ^
        -spike_free 0.5 ^
        -step 0.15 ^
        -hillshade ^
        -o zurich_sf.png

The differences between a first-return DSM and a spike-free DSM are most drastic along building roofs and in vegetated areas. To inspect in more detail the differences between a first-return and our spike-free TIN we use lasview that allows to iteratively visualize the construction process of a spike-free TIN.

lasview -i ..\data\france.laz -spike_free 0.9

Pressing <f> and <t> constructs the first-return TIN. Pressing <SHIFT> + <t> destroys the first-return TIN. Pressing <SHIFT> + <y> constructs the spike-free TIN. Pressing <y> once destroys the spike-free TIN. Pressing <y> many times iteratively constructs the spike-free TIN.

One crucial piece of information is still missing. What value should you use as the freeze constraint of the spike-free algorithm that we set to 0.9 for “france.laz” and to 0.5 for “zurich.laz” as the argument to the command-line option ‘-spike_free’. The optimal value is related to the expected edge-length and we found the 99th percentile of a histogram of edge lengths of the last-return TIN to be useful. Or simpler … try a value that is about three times the average pulse spacing.

Khosravipour, A., Skidmore, A.K., Isenburg, M. and Wang, T.J. (2015) Development of an algorithm to generate pit-free Digital Surface Models from LiDAR, Proceedings of SilviLaser 2015, pp. 247-249, September 2015.
Khosravipour, A., Skidmore, A.K., Isenburg, M (2016) Generating spike-free Digital Surface Models using raw LiDAR point clouds: a new approach for forestry applications, (journal manuscript under review).