Leaked: “Classified LiDAR” of Pentagon in LAS 1.4 Format

LiDAR leaks have happened! Black helicopters are in the sky!  A few days ago a tiny tweet leaked the online location of “classified LiDAR” for Washington, DC. This LiDAR really is “classified” and includes an aerial scan of the Pentagon. For rogue scientists world-wide we offer a secret download link. It links to a file code-named ‘pentagon.laz‘ that contains the 8,044,789 “classified” returns of the Pentagon shown below. This “classified file” can be deciphered by any software with native LAZ support. It was encrypted with the “LAS 1.4 compatibility mode” of LASzip. The original LAS 1.4 content was encoded into a inconspicuous-looking LAZ file. New point attributes (such as the scanner channel) were hidden as “extra bytes” for fully lossless encryption. Use ‘laszip‘ to fully decode the original “classified” LAS 1.4 file … (-;

Seriously, a tiled LiDAR data set for the District of Columbia flown in 2015 is available for anyone to use on Amazon S3 with a very permissive open data license, namely the Creative Commons Attribution 3.0 License. The LiDAR coverage can be explored via this interactive map. The tiles are provided in LAS 1.4 format and use the new point type 6. We downloaded a few tiles near the White House, the Capitol, and the Pentagon to test the “native LAS 1.4 extension” of our LASzip compressor which will be released soon (a prototype for testing is already available). As these uncompressed LAS files are YUUUGE we use the command line utility ‘wget‘ for downloading. With option ‘-c’ the download continues where it left off in case the transfer gets interrupted.

LiDAR pulse density from 20 or less (blue) to 100 or more (red) pulses per square meter.

We use lasboundary to create labeled bounding boxes for display in Google Earth and lasgrid to a create false color visualization of pulse density with the command lines shown below. Pulse densities of 20 or below are mapped to blue. Pulse densities of 100 or above are mapped to red. We picked the min value 20 and the max value 100 for this false color mapping by running lasinfo with the ‘-cd’ option to compute an average pulse density and then refining the numbers experimentally. We also use lasoverlap to visualize how flightlines overlap and how well they align. Vertical differences of up to 20 cm are mapped to white and differences of 40 cm or more are mapped to saturated blue or red.

lasboundary -i *.las ^
            -use_bb ^
            -labels ^
            -odir quality -odix _bb -okml

lasgrid -i *.las ^
        -keep_last ^
        -point_density -step 2 ^
        -false -set_min_max 20 100 ^
        -odir quality -odix _d_20_100 -opng ^
        -cores 2

lasoverlap -i *.las ^
           -min_diff 0.2 -max_diff 0.4 ^
           -odir quality -opng ^
           -cores 2

The visualization of the pulse density and of the flightline overlap both show that there is no LiDAR for the White House or Capitol Hill. We will never know how tall the tomato and kale plants had grown in Michelle Obama’s organic garden on that day. Note that the White House and Capitol Hill were not simply “cut out”. Instead the flight plan of the survey plane was carefully designed to avoid these areas. Surprisingly, the Pentagon did not receive the same treatment and is (almost) fully included in the open LiDAR as mentioned in the dramatic first paragraph. Interesting is how the varying (tidal?) water level of the Potomac River shows up in the visualization of flightline miss-alignments.

There are a number of issues in these LiDAR files. The most serious ones are reported at the very end of this article. We will now scrutinize the partly-filled tile 2016.las close to the White House with only 11,060,334 returns. A lasvalidate check immediately reports three deviations from the LAS 1.4 specification:

lasvalidate -i 2016.las -o 2016_check.xml
  1. For proper LAS 1.4 files containing point type 6 through 10 all ‘legacy’ point counts in the LAS header should be set to 0. The following six fields in the LAS header should be zero for tile 2016.las (and all other tiles):
    + legacy number of point records
    + legacy number of points by return[0]
    + legacy number of points by return[1]
    + legacy number of points by return[2]
    + legacy number of points by return[3]
    + legacy number of points by return[4]
  2. There should not be any LiDAR return in a valid LAS file whose ‘number of returns of given pulse’ attribute is zero but there are 8 such points in tile 2016.las (and many more in various other tiles).
  3. There should not be any LiDAR return whose ‘return number’ attribute is larger than their ‘number of returns of given pulse’ attribute but there are 8 such points in tile 2016.las (and many more in various other tiles).

The first issue is trivial. There is an efficient in-place fix that does not require to rewrite the entire file using lasinfo with the following command line:

lasinfo -i 2016.las ^
        -nh -nv -nc ^
        -set_number_of_point_records 0 ^
        -set_number_of_points_by_return 0 0 0 0 0 ^

A quick check with las2txt shows us that the second and third issue are caused by the same eight points. Instead of writing an 8 for the ‘number of returns’ attribute the LAS file exporter must have written a 0 (marked in red for all eight returns) and instead of writing an 8 for the ‘return number’ attribute the LAS file exporter must have written a 1 (also marked in red). We can tell it from the true first return via its z coordinate (marked in blue) as the last return should be the lowest of all.

las2txt -i 2016.las ^
        -keep_number_of_returns 0 ^
        -parse xyzrnt ^
        -stdout
397372.70 136671.62 33.02 4 0 112813299.954811
397372.03 136671.64 28.50 5 0 112813299.954811
397371.28 136671.67 23.48 6 0 112813299.954811
397370.30 136671.68 16.86 7 0 112813299.954811
397369.65 136671.70 12.50 1 0 112813299.954811
397374.37 136671.58 44.17 3 0 112813299.954811
397375.46 136671.56 51.49 1 0 112813299.954811
397374.86 136671.57 47.45 2 0 112813299.954811

With las2las we can change the ‘number of returns’ from 0 to 8 using a ‘-filtered_transform’ as illustrated in the command line below. We suspect that higher number of returns such as 9 or 10 might have been mapped to 1 and 2. Fixing those as well as repairing the wrong return numbers will require a more complex tool. We would recommend to check all tiles with more scrutiny using the lasreturn tool. But wait … more return numbering issues are to come.

las2las -i 2016.las ^
        -keep_number_of_returns 0 ^
        -filtered_transform ^
        -set_extended_number_of_returns 8 ^
        -odix _fixed -olas

A closer look at the scan pattern reveals that the LiDAR survey was flown with a dual-beam system where two laser beams scan the terrain simultaneously. This is evident in the textual representation below as there are multiple “sets of returns” for the same GPS time stamp such as 112813952.110394. We group the returns from the two beams into an orange and a green group. Their coordinates show that the two laser beams point into different directions when they are simultaneously “shot” and therefore hit the terrain far apart from another.

las2txt -i 2016.las ^
        -keep_gps_time 112813952.110392 112813952.110396 ^
        -parse xyzlurntp ^
        -stdout
397271.40 136832.35 54.31 0 0 1 1 112813952.110394 117
397277.36 136793.35 38.68 0 1 1 4 112813952.110394 117
397277.35 136793.56 32.89 0 1 2 4 112813952.110394 117
397277.34 136793.88 24.13 0 1 3 4 112813952.110394 117
397277.32 136794.25 13.66 0 1 4 4 112813952.110394 117

The information about which point is from which beam is currently stored into the generic ‘user data’ attribute instead of into the dedicated ‘scanner channel’ attribute. This can be fixed with las2las as follows.

las2las -i 2016.las ^
        -copy_user_data_into_scanner_channel ^
        -set_user_data 0 ^
        -odix _fixed -olas

Unfortunately the LiDAR files have much more serious issues in the return numbering. It’s literally a “Total Disaster!” and “Sad!” as the US president will tweet shortly. After grouping all returns with the same GPS time stamp into an orange and a green group there is one more set of returns left unaccounted for.

las2txt -i 2016.las ^
        -keep_gps_time 112813951.416451 112813951.416455 ^
        -parse xyzlurntpi ^
        -stdout
397286.02 136790.60 45.90 0 0 1 4 112813951.416453 117 24
397286.06 136791.05 39.54 0 0 2 4 112813951.416453 117 35
397286.10 136791.51 33.34 0 0 3 4 112813951.416453 117 24
397286.18 136792.41 21.11 0 0 4 4 112813951.416453 117 0
397286.12 136791.75 30.07 0 0 1 1 112813951.416453 117 47
397291.74 136750.70 45.86 0 1 1 1 112813951.416453 117 105
las2txt -i 2016.las ^
        -keep_gps_time 112813951.408708 112813951.408712 ^
        -parse xyzlurntpi ^
        -stdout
397286.01 136790.06 45.84 0 0 1 4 112813951.408710 117 7
397286.05 136790.51 39.56 0 0 2 4 112813951.408710 117 15
397286.08 136790.96 33.33 0 0 3 4 112813951.408710 117 19
397286.18 136792.16 17.05 0 0 4 4 112813951.408710 117 0
397286.11 136791.20 30.03 0 0 1 2 112813951.408710 117 58
397286.14 136791.67 23.81 0 0 2 2 112813951.408710 117 42
397291.73 136750.16 45.88 0 1 1 1 112813951.408710 117 142

This can be visualized with lasview and the result is unmistakably clear: The return numbering is messed up. There should be one shot with five returns (not a group of four and a single return) in the first example. And there should be one shot with six returns (not a group of four and a group of two returns) in the second example. Such a broken return numbering results in extra first (or last) returns. These are serious issues that affect any algorithm that relies on the return numbering such as first-return DSM generation or canopy cover computation. Those extra returns will also make the pulse density appear higher and the pulse spacing appear tighter than they really are. The numbers from 20 (blue) to 100 (red) pulses per square meters in our earlier visualization are definitely inflated.

lasview -i 2016.las ^
        -keep_gps_time 112813951.416451 112813951.416455 ^
        -color_by_return

lasview -i 2016.las ^
        -keep_gps_time 112813951.408708 112813951.408712 ^
        -color_by_return

After all these troubles here something nice. Side-by-side a first-return TIN and a spike-free TIN (using a freeze of 0.8 m) of the center court cafe in the Pentagon. Especially given all these “fake first returns” in the Washington DC LiDAR we really need the spike-free algorithm to finally “Make a DSM great again!” … (-;

We would like to acknowledge the District of Columbia Office of the Chief Technology Officer (OCTO) for providing this data with a very permissive open data license, namely the Creative Commons Attribution 3.0 License.

 

NRW Open LiDAR: Merging Points into Proper LAS Files

In the first part of this series we downloaded, compressed, and viewed some of the newly released open LiDAR data for the state of North Rhine-Westphalia. In the second part we look at how to merge the multiple point clouds provided back into single LAS or LAZ files that are as proper as possible. Follow along with a recent version of LAStools and a pair of DGM and DOM files for your area of interest. For downloading the LiDAR we suggest using the wget command line tool with option ‘-c’ that after interruption in transmission will restart where it left off.

In the first part of this series we downloaded the pair of DGM and DOM files for the City of Bonn. 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 with centimeter resolution. We had already converted these textual *.xyz files into binary *.laz files. We did this with the open source LASzip compressor that is distributed with LAStools as described in that blog post. We continue now with the assumption that you have converted all of the *.xyz files to *.laz files as described here.

Mapping from tile names of DGM and DOM archives to classification and return type of points.

The mapping from tile names in DGM and DOM archives to the classification and return type of points: lp = last return. fp = first return, ab,aw,ag = synthetic points

There are multiple tiles for each square kilometer as the LiDAR has been split into different files based on classification and return type. Furthermore there are also synthetic points that were created by the land survey department to replace LiDAR under bridges and along buildings for generating higher quality rasters. We want to combine all points of a square kilometer into a single LAZ tile as it is usually expected. Simply merging the multiple files per tile is not an option as this would result in loosing point classifications and return type information as well as in duplicating all single returns that are stored in more than one file. The folks at OpenNRW offer this helpful graphic to know what the acronyms above mean:

Illustration of how acronyms used in tile names correspond to point classification and type.

Illustration of how acronyms used in tile names correspond to point classification and type.

In the following we’ll be looking at the set of files corresponding to the UTM tile 32366 / 5622. We wanted an interesting area with large buildings, a bridge, and water. We were looking for a suitable area using the KML overlays generated in part one. The tile along the Rhine river selected in the picture below covers the old city hall, the opera house, and the “Kennedy Bridge” has a complete set of DGM and DOM files:

      3,501 dgm1l-ab_32366_5622_1_nw.laz
     16,061 dgm1l-ag_32366_5622_1_nw.laz
      3,269 dgm1l-aw_32366_5622_1_nw.laz
    497,008 dgm1l-brk_32366_5622_1_nw.laz
  7,667,715 dgm1l-lpb_32366_5622_1_nw.laz
 12,096,856 dgm1l-lpnb_32366_5622_1_nw.laz
     15,856 dgm1l-lpub_32366_5622_1_nw.laz

      3,269 dom1l-aw_32366_5622_1_nw.laz
 21,381,106 dom1l-fp_32366_5622_1_nw.laz
We find the name of the tiles that cover the "Kennedy Bridge" using the KML overlays generated in part one.

We find the name of the tile that covers the “Kennedy Bridge” using the KML overlays generated in part one.

We now assign classification codes and flags to the returns from the different files using las2las, merge them together with lasmerge, and recover single, first, and last return information with lasduplicate. We set classifications to bridge deck (17), ground (2), to unclassified (1), and to noise (7) for all returns in the files with the acronym ‘brk’ (= bridge points), the acronym ‘lpb’ (= last return ground), the acronym ‘lpnb’ (= last return non-ground), and the acronym ‘lpub’ (= last return under ground). with las2las and store the resulting files to a temporary folder.

las2las -i dgm1l-brk_32366_5622_1_nw.laz ^
        -set_classification 17 ^
        -odir temp -olaz

las2las -i dgm1l-lpb_32366_5622_1_nw.laz ^
        -set_classification 2 ^
        -odir temp -olaz

las2las -i dgm1l-lpnb_32366_5622_1_nw.laz ^
        -set_classification 1 ^
        -odir temp -olaz

las2las -i dgm1l-lpub_32366_5622_1_nw.laz ^
        -set_classification 7 ^
        -odir temp -olaz

Next we use the synthetic flag of the LAS format specification to flag any additional points that were added (no measured) by the survey department to generate better raster products. We set classifications to ground (2) and the synthetic flag for all points of the files with the acronym ‘ab’ (= additional ground) and the acronym ‘ag’ (= additional building footprint). We set classifications to water (9) and the synthetic flag for all points of the files with the acronym ‘aw’ (= additional water bodies). Files with acronym ‘aw’ appear both in the DGM and DOM archive. Obviously we need to keep only one copy.

las2las -i dgm1l-ab_32366_5622_1_nw.laz ^
        -set_classification 2 ^
        -set_synthetic_flag 1 ^
        -odir temp -olaz

las2las -i dgm1l-ag_32366_5622_1_nw.laz ^
        -set_classification 2 ^
        -set_synthetic_flag 1 ^
        -odir temp -olaz

las2las -i dgm1l-aw_32366_5622_1_nw.laz ^
        -set_classification 9 ^
        -set_synthetic_flag 1 ^
        -odir temp -olaz

Using lasmerge we merge all returns from files with acronyms ‘brk’ (= bridge points), ‘lpb’ (= last return ground),  ‘lpnb’ (= last return non-ground), and ‘lpub’ (= last return under ground) into a single file that will then contain all of the (classified) last returns for this tile.

lasmerge -i temp\dgm1l-brk_32366_5622_1_nw.laz ^
         -i temp\dgm1l-lpb_32366_5622_1_nw.laz ^
         -i temp\dgm1l-lpnb_32366_5622_1_nw.laz ^
         -i temp\dgm1l-lpub_32366_5622_1_nw.laz ^
         -o temp\dgm1l-lp_32366_5622_1_nw.laz

Next we run lasduplicate three times to recover which points are single returns and which points are the first and the last return of a pair of points generated by the same laser shot. First we run lasduplicate with option ‘-unique_xyz’ to remove any xyz duplicates from the last return file. We also mark all surviving returns as the second of two returns. Similarly, we remove any xyz duplicates from the first return file and mark all survivors as the first of two returns. Finally we run lasduplicate with option ‘-single_returns’ with the unique last and the unique first return files as ‘-merged’ input. If a return with the exact same xyz coordinates appears in both files only the first copy is kept and marked as a single return. In order to keep the flags and classifications from the last return file, the order in which the last and first return files are listed in the command line is important.

lasduplicate -i temp\dgm1l-lp_32366_5622_1_nw.laz ^
             -set_return_number 2 -set_number_of_returns 2 ^
             -unique_xyz ^
             -o temp\last_32366_5622_1_nw.laz

lasduplicate -i dom1l-fp_32366_5622_1_nw.laz ^
             -set_return_number 1 -set_number_of_returns 2 ^
             -unique_xyz ^
             -o temp\first_32366_5622_1_nw.laz

lasduplicate -i temp\last_32366_5622_1_nw.laz ^
             -i temp\first_32366_5622_1_nw.laz ^
             -merged ^
             -single_returns ^
             -o temp\all_32366_5622_1_nw.laz

We then add the synthetic points with another call to lasmerge to obtain a LAZ file containing all points of the tile correctly classified, flagged, and return-numbered.

lasmerge -i temp\dgm1l-ab_32366_5622_1_nw.laz ^
         -i temp\dgm1l-ag_32366_5622_1_nw.laz ^
         -i temp\dgm1l-aw_32366_5622_1_nw.laz ^
         -i temp\all_32366_5622_1_nw.laz ^
         -o temp\merged_32366_5622_1_nw.laz

Optional: For more efficient use of this file in subsequent processing – and especially to accelerate area-of-interest queries with lasindex – it is often of great advantage to reorder the points in a spatially coherent manner. A simple call to lassort will rearrange the points along a space-filling curve such as a Hilbert curve or a Z-order curve.

lassort -i temp\merged_32366_5622_1_nw.laz ^
        -o bonn_32366_5622_1_nw.laz

Note that we also renamed the file because a good name can be useful if you find that file again in two years from now. Let’s have a look at the result with lasview.

lasview -i bonn_32366_5622_1_nw.laz

In lasview you can press <c> repeatedly to switch through all available coloring modes until you see the yellow (single) / red (first) / last (blue) coloring of the returns. The recovered return types are especially evident under vegetation, in the presence of wires, and along building edges. Press <x> to select an area of interest and press <x> again to inspect it more closely. Press <i> while hovering above a point to show its coordinates, classification, and return type.

We did each processing in separate steps to illustrate the overall workflow. The above sequence of LAStools command line calls can be shortened by combining multiple processing steps into one operation. This is left as an exercise for the advanced LAStools user … (-;

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 … (-:

Second German State Goes Open LiDAR

The floodgates of open geospatial data have opened in Germany. Days after reporting about the first state-wide release of open LiDAR, we are happy to follow up with a second wonderful open data story. The state of Thuringia (Thüringen) – also called the “green heart of Germany” – has also implemented an open geospatial data policy. This had already been announced in March 2016 but must have gone online just now. A reader of our last blog article pointed this out in the comments. And it’s not just LiDAR. You can download:

It all comes with the same permissible license as OpenNRW’s data. This is open data madness! Everything you could possibly hope for presented via a very functional download portal. Kudos to TLVermGeo (“Thüringisches Landesamt für Vermessung und Geoinformation”) for creating an open treasure cove of free-for-all geospatial data.

Let us have a look at the LiDAR. We use the interactive portal to zoom to an area of interest. With the recent rise of demagogues it cannot hurt to look at a stark reminder of where such demagoguery can lead. In his 1941 play “The Resistible Rise of Arturo Ui” – a satirical allegory on the rise of Adolf Hitler – Bertolt Brecht writes “… don’t rejoice too soon at your escape. The womb he crawled from is still going strong.”

We are downloading LiDAR data around the Buchenwald concentration camp. According to Wikipedia, it was established in July 1937 and was one of the largest on German soil. Today the remains of Buchenwald serve as a memorial and as a permanent exhibition and museum.

We download the 15 tiles surrounding the blue one: two on its left, two on its right and one corresponding row of five tiles above and below. Each of the 15 zipped archives contains a *.laz file and *.meta file. The *.laz file contains the LiDAR points and *.meta file contains the textual information below where “Lage” and “Höhe” refer to “horizontal” and “vertical”:

Datei: las_655_5653_1_th_2010-2013.laz
Erfassungsdatum: 2011-03
Erfassungsmethode: Airborne Laserscanning
Lasergebiet: Laser_04_2010
EPSG-Code Lage: 25832
EPSG-Code Höhe: 5783
Quasigeoid: GCG2005
Genauigkeit Lage: 0.12m
Genauigkeit Höhe: 0.04m
Urheber: (c) GDI-Th, Freistaat Thueringen, TLVermGeo

Next we will run a few quality checks on the 15 tiles by processing them with lasinfolasoverlap, lasgrid, and las2dem. We output all results into a folder named ‘quality’.

With lasinfo we create one text file per tile that summarizes its contents. The ‘-cd’ option computes the all return and last return density. The ‘-histo point_source 1’ option produces a histogram of point source IDs that are supposed to store which flight line each return came from. The ‘-odir’ and ‘-odix’ options specify the directory for the output and an appendix to the output file name. The ‘-cores 4’ option starts 4 processes in parallel, each working on a different tile.

lasinfo  -i las_*2010-2013.laz ^
         -cd ^
         -histo point_source 1 ^
         -odir quality -odix _info -otxt ^
         -cores 4

If you scrutinize the resulting text files you will find that the average last return density ranges from 6.29 to 8.13 and that the point source IDs 1 and 9999 seem to encode some special points. Likely those are synthetic points added to improve the derived rasters similar to the “ab”, “ag”, and “aw” files in the OpenNRW LiDAR. Odd is the lack of intermediate returns despite return numbers ranging all the way up to 7. Looks like only the first returns and the last returns are made available (like for the OpenNRW LiDAR). That will make those a bit sad who were planning to use this LiDAR for forest or vegetation mapping. The header of the *.laz files does not store geo-referencing information, so we will have to enter that manually. And the classification codes do not follow the standard ASPRS assignment. In red is our (currently) best guess what these classification codes mean:

[...]
histogram of classification of points:
 887223 ground (2) ground
 305319 wire guard (13) building
 172 tower (15) bridges
 41 wire connector (16) synthetic ground under bridges
 12286 bridge deck (17) synthetic ground under building
 166 Reserved for ... (18) synthetic ground building edge
 5642801 Reserved for ... (20) non-ground
[...]

With lasoverlap we can visualize how much overlap the flight lines have and the (potential miss-)alignment between them. We drop the synthetic points with point source IDs 1 and 9999 and add geo-referencing information with ‘-epsg 25832’ so that the resulting images can be displayed as Google Earth overlays. The options ‘-min_diff 0.1’ and ‘-max_diff 0.4’ map elevation differences of up +/- 10 cm to white. Above +/- 10 cm the color becomes increasingly red/blue with full saturation at +/- 40 cm or higher. This difference can only be computed for pixels with two or more overlapping flight lines.

lasoverlap  -i las_*2010-2013.laz ^
            -drop_point_source 1 ^
            -drop_point_source 9999 ^
            -min_diff 0.1 -max_diff 0.4 ^
            -odir quality -opng ^
            -epsg 25832 ^
            -cores 4

With lasgrid we check the density distribution of the laser pulses by computing the point density of the last returns for each 2 by 2 meter pixel and then mapping the computed density value to a false color that is blue for a density of 0 and red for a density of 10 or higher.

lasgrid  -i las_*2010-2013.laz ^
         -drop_point_source 1 ^
         -drop_point_source 9999 ^
         -keep_last ^
         -step 2 -point_density ^
         -false -set_min_max 0 10 ^
         -odir quality -odix _d_0_10 -opng ^
         -epsg 25832 ^
         -cores 4
Pulse density variation due to flight line overlap and flight turbulence.

Pulse density variation due to flight line overlap is expected. But also the contribution of flight turbulence is quite significant.

With las2dem we can check the quality of the already existing ground classification in the LiDAR by producing a hillshaded image of a DTM for visual inspection. Based on our initial guess on the classification codes (see above) we keep those synthetic points that improve the DTM (classification codes 16, 17, and 18) in addition to the ground points (classification code 2).

las2dem  -i las_*2010-2013.laz ^
         -keep_class 2 16 17 18 ^
         -step 1 ^
         -hillshade ^
         -odir quality -odix _shaded_dtm -opng ^
         -epsg 25832 ^
         -cores 4
Problems in the ground classification of LiDAR points are often visible in a hillshaded DTM raster.

Problems in the ground classification of LiDAR points are often visible in a hillshaded DTM.

Wow. We see a number of ground disturbances in the resulting hillshaded DTM. Some of them are expected because if you read up on the history of the Buchenwald concentration camp you will learn that in 1950 large parts of the camp were demolished. However, the laser finds the remnants of those barracks and buildings as clearly visible ground disturbances under the canopy of the dense forest that has grown there since. And then there are also these bumps that look like bomb craters. Are those from the American bombing raid on August 24, 1944?

We are still not entirely sure what those “bumps” arem but our initially assumption that all of those would have to be bomb craters from that fatal American bombing raid on August 24, 1944 seems to be wrong. Below is a close-up with lasview of the triangulated and shaded ground points from the lower right corner of tile ‘las_656_5654_1_th_2010-2013.laz’.

Close-up in lasview on the bumbs in the ground.

Close-up in lasview on the bumbs in the ground.

We are not sure if all the bumps we can see here are there for the same reason. But we found an old map and managed to overlay it on Google Earth. It suggest that at least the bigger bumps are not bomb craters. On the map they are labelled as “Erdfälle” which is German for “sink hole”.

We got a reminder on the danger of demagogues as well as a glimpse into conflict archaeology and geomorphology with this open LiDAR download and processing exercise. If you want to explore this area any further you can either download the LiDAR and download LAStools and process the data yourself or simply get our KML files here.

Acknowledgement: The LiDAR data of TLVermGeo 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 “geoportal-th.de (2017)” with the year of the download in brackets and should specify the Universal Resource Identification (URI). We have not found this yet and use this URL as a placeholder until we know the correct one. Done. So easy. Thank you, geoportal Thüringen … (-:

NRW Open LiDAR: Download, Compression, Viewing

UPDATE: (March 6th): Second part merging Bonn into proper LAS files

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 dgm1l-lpb_32360_5613_1_nw.xyz
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 or look at 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 ^
            -gui
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 … (-:

First Open LiDAR in Germany

UPDATE: (January 6th): Our new tutorial “downloading Bonn in LiDAR“.
UPDATE: (January 9th): Now a second state went open LiDAR as well.
UPDATE: (March 6th): New tutorial merging Bonn into proper LAS files

Kudos to OpenNRW for offering online download links for hundreds of Gigabytes of open LiDAR for the entire state of North Rhine-Westphalia (Nordrhein-Westfalen) as announced a few months ago:

More and more countries, states, and municipalities are deciding to make their LiDAR archives accessible to the general public. Some are doing entirely for free with instant online download and a generous open license that allows data sharing and commercial use. Others still charge a “small administrative fee” and require filling our actual paperwork with real signatures in ink and postal mailing of hard drives that can easily take half a year to complete. Some licences are also stricter in terms of data sharing and commercial use.

And then there was Germany where all the LiDAR data has traditionally been locked up in some cave by the 16 state survey departments and was sold for more than just a fee. Financial reasons would usually prohibit residents in Germany from making, for example, an elevation profile for their favorite mountain bike trail for hobby purposes. Or starting a small side business that (for 5 Euro a sheet) sells “Gassi Maps” with elevation-optimized dog walking paths of low incline that go past suitable potty spots and dog-friendly coffee shops.

The reason that many national and state mapping agencies have opened their LiDAR holdings for free and unencumbered access are manifold. A previous blog post had looked at the situation in England whose Environment Agency also used to sell LiDAR data and derivatives. The common argument that government agencies have been using to justify selling data (paid for with taxes) to those very same tax payers was that this would be used to finance future surveys.

It was the “freedom of information” request by Louise Huby on November 21st, 2014 that exposed this as a flawed argument. The total amount of revenue generated for all LiDAR and derivatives sales by the Environment Agency was just around £323,000 per year between 2007 and 2014. This figure was dwarfed by its annual operational budget of £1,025,000,000 in 2007/08. The revenue from LiDAR sales was merely 0.03 percent of the agencies’s budget. That can maybe pay for free coffee and tea in the office, but not for future airborne LiDAR flights. The reaction was swift. In September 2015 the Environment Agency made all their DTM and DSM rasters down to 0.25 meter resolution available online for open access and in March 2016 added the raw point clouds for download in LAZ format with a very permissible license. It has since been an incredible success story and the Environment Agency has been propelled into the role of a “champion for open data” as sketched in my ACRS 2016 keynote talk that is available on video here.

Frustrated with the situation in Germany and inspired by the change in geospatial data policy in England, we have been putting in similar “Frag den Staat” freedom of information requests with 11 of the 16 German state mapping agencies, asking about how much sales revenue was generated annually from their LiDAR and derivative sales. These four states denied the request:

We never heard back from Lower Saxony (Niedersachsen) and Thuringia (Thüringen) wanted more fees than we were willing to spend on this, but our information requests were answered by five states that are here listed with the average amount of reported revenue per year in EUR:

LiDAR acquisitions are expensive and while it would be interesting to also find out how much each state has actually spent on airborne surveys over the past years (another “Frag den Staat” request anyone?), it is obvious that the reported revenues are just a tiny fraction of those costs. Exact details of the reported revenue per year can be accessed via the links to the information requests above. The cost table of each answer letter is copied below.

However, it’s not all bad news in Germany. Some of you may have seen my happy announcement of the OpenNRW initiative that come January 2017 this would also include the raw LiDAR points. And did it happen? Yes it did! Although the raw LiDAR points are maybe a little tricky to find, they are available for free download and they come with a very permissible license.

The license 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 merely requires you to properly name the source. For the LiDAR you need to list the “Land NRW (2017)” with the year of the download in brackets as the source and specify the data set that was used via the respective Universal Resource Identification (URI) for the DOM and/or the DGM.

The OpenNRW portal now also offers the download of the LiDAR "punktwolke" (German for point cloud).

The OpenNRW portal now also offers the download of the LiDAR “punktwolke” (German for point cloud).

Follow this link to get to the open data download portal. Now type in “punktwolke” (German for “point cloud”) into the search field and on this January 3rd 2017 that gives me 2 “Ergebnisse” (German for “results”). The LiDAR point cloud representation is a bit unusual by international standards. One link is for the DGM (German for DTM) and the other for the DOM (German for DSM). Both links are eventually leading you to unstructured LiDAR point clouds that are describing these surfaces. But it’s still a little tricky to find them. First click on the “ATOM” links that get you to XML description with meta information and a lot of links for the DTM and the DSM. Somewhere hidden in there you find the actual download links for hundreds of Gigabytes of LiDAR for the entire state of North Rhine-Westphalia (Nordrhein-Westfalen):

We download the two smallest zipped files DGM and DOM for the municipality of Wickede (Ruhr) to have a look at the data. The point cloud is in EPSG 5555 which is a compound datum of horizontal EPSG 25832 aka ETRS89 / UTM zone 32N and vertical EPSG 5783 aka the “Deutches Haupthohennetz 1992”.

The contents of the DGM zip file contains multiple files per tile.

The contents of the DGM zip file contains multiple files per tile.

The DGM zip file has a total of 212 *.xyz files that list the x, y, and z coordinate for each point in ASCII format. We first compress them and add the EPSG 25832 code with laszip. The compressed LAZ files are less than half the size of the zipped XYZ files. Each file corresponds to a particular square kilometer. The name of each tile contains the lower left coordinate of this square kilometer but there can be multiple files for each square kilometer:

  • 14 files with “ab” in the name contain very few points. They look like additional points for under bridges. The “b” is likely for “Brücke” (German for “bridge”).
  • 38 files with “ag” in the name contain seem to contain only points in areas where buildings used to cover the terrain but with ground elevation. The “g” is likely for “Gebäude” (German for “building”).
  • 30 files with “aw” in the name contain seem to contain only points in areas where there are water bodies but with ground elevation. The “w” is likely for “Wasser” (German for “water”).
  • 14 files with “brk” in the name also contain few points. They look like the original bridge point that are replaced by the points in the files with “ab” in the name to flatten the bridges. The “brk” is also likely for “Brücke” (German for “bridge”).
  • 42 files with “lpb” in the name. They look like the last return LiDAR points that were classified as ground. The “lpb” is likely for “Letzter Pulse Boden” (German for “last return ground”).
  • 42 files with “lpnb” in the name. They look like those last return LiDAR points that were classified as non-ground. The “lpnb” is likely for “Letzter Pulse Nicht Boden” (German for “last return not ground”).
  • 32 files with “lpub” in the name contain very few points. They look like the last return points that are too low and were therefore excluded. The “lpub” is likely for “Letzter Pulse Unter Boden” (German for “last return under ground”).

It is left to an exercise to the user for figure out which of those above sets of files should be used for generating a raster DTM. (-: Give us your ideas in the comments. The DOM zip file has a total of 72 *.xyz files. We also compress them and add the EPSG 25832 code with laszip. The compressed LAZ files are less than half the size of the zipped XYZ files. Again there are multiple files for each square kilometer:

  • 30 files with “aw” in the name contain seem to contain only points in areas where there are water bodies but with ground elevation. The “w” is likely for “Wasser” (German for “water”).
  • 42 files with “fp” in the name. They look like the first return LiDAR points. The “fp” is likely for “Frühester Pulse” (German for “first return”).

If you use all these points from the DOM folder your get the nice DSM shown below … albeit not a spike-free one.

A triangulated first return DSM generated mainly from the file "dom1l-fp_32421_5705_1_nw.laz" with the points from "dom1l-aw_32421_5705_1_nw.laz" for areas with water bodies shown in yellow.

A triangulated first return DSM generated mainly from the file “dom1l-fp_32421_5705_1_nw.laz” with the points from “dom1l-aw_32421_5705_1_nw.laz” for areas with water bodies shown in yellow.

Kudos to OpenNRW for being the first German state to open their LiDAR holdings. Which one of the other 15 German state survey departments will be next to promote their LiDAR as open data. If you are not the last one to do so you can expect to get featured here too … (-;

UPDATE (January 5th): The folks at OpenNRW just tweeted us information about the organization of the zipped archives in the DTM (DGM) and DSM (DOM) folders. We guessed pretty okay which points are in which file but the graphic below (also available here) summarizes it much more nicely and also tells us that “a” was for “ausgefüllt” (German for “filled up”). Maximally two returns per pulse are available: either a single return or a first return plus a last return. There are no intermediate returns, which may be an issue for those interested in vegetation mapping.

Illustration of which LiDAR point is in which file.

Nice illustration of which LiDAR point is in which file. All files with ‘ab’, ‘ag’, or ‘aw’ in the name contain synthetic points that fill up ground areas not properly reached by the laser.

Restricting Access to National LiDAR: Is it worth it?

Policy makers in many countries are debating whether it is worthwhile to open their National LiDAR holdings – collected with tax-payer money – for free (or cheap) open access or whether they should continue to restrict access and charge potential users of the data to recoup expenses. Most would agree that hurdle-free, instant online access allows to exploit this valuable resource to the fullest and to maximize its benefit to the citizens. But some argue that opening this data eliminates the revenue stream the government needs to finance future surveys. Is this really true? No!

On November 21st, 2014 Louise Huby made this “Freedom of Information” request to the Environment Agency which are responsible for collecting, processing, and selling LiDAR data and derivatives for England with a focus on flood mapping applications. This seems to mainly be handled by the Environment Agency Geomatics, a specialist business unit within the Environment Agency.

Dear Environment Agency,
Please could you provide me with a breakdown by year of
all revenue made from the sale of LiDAR Data?
Yours faithfully,
Louise Huby

Later she added

Please could you provide me with a breakdown by year
of all revenue made from the sale of LiDAR Data?
Could you provide me with the information requested for
the following years: 2004, 2005, 2006, 2007, 2008, 2009,
2010, 2011, 2012, 2013, 2014.

A week later, on November 28th, Louise Huby received this answer:

EA_annual_sales_turnover

Hence the annual sales turnover (*) for LiDAR data was around £323,000 per year between 2007 and 2014.  It would be interesting to know how the annual average of £323,000 divides up further in terms of delivered products (raw LiDAR points or derivative), individual sale volume, price per square kilometer, type of customer, … Another request anyone?

(*) Sales turnover is the total amount of revenue generated during the calculation period. The revenue included in this calculation is from both cash sales and credit sales.

According to Wikipedia, the Environment Agency had an operational budget of £1,025,000,000 in 2007/08 (about half of this for flood risk management). This means the renevue from LiDAR sales is equivalent to 0.03 percent of the Agencies’s operating budget. I find this number shockingly low. Is this meager sales revenue an acceptable reason to keep the LiDAR locked up and inaccessible to the public?

Finland opened its LiDAR data in May 2012 and Denmark freed their national holdings in March 2013. Also Holland has turned around 180 degrees on its original LiDAR access policy by releasing its comprehensive AHN2 National LiDAR data set (including the raw point cloud) as free and open online data that was at first guarded and sold at high cost. I was given several reasons for this:
  • The main customer of LiDAR are the many branches of government from municipalities to federal agencies who often did not use it before, because it was “too difficult to obtain”. If someone needed geospatial data to make a decision but first had to fill out lots of paperwork and justification forms, possibly find a budget, and wait weeks for the delivery, then many questions had become irrelevant by the time the data arrived and decision were made without or with less good data.
  • Open LiDAR brings a bigger “return of investment” because the data – whose value is highly inflationary – gets used immediately for all purposes and not only when the anticipated potential of exploiting it sufficiently outweighs the upfront investment in time and money for aquiring it.
  • New business cases become worthwhile for which the LiDAR becomes “raw material” used to create new products and provide new services. This creates additional high-tech companies that pay taxes and desirable high-skilled jobs that would otherwise not exist. This can only happen if the resource “LiDAR” is either free or very cheap. It benefits both government and citizens as additional services and products are becoming available.
  • Overall: faster, better, cheaper service for all, full exploitation of the available resource, and higher return (even financially) in the long run.
One of the biggest drawbacks of trying to monetize the LiDAR is not only that there will be fewer services and products created by private sector businesses … but they will also be much more expensive. Those companies that took the risk to buy the data in the first place will charge a high premium on their services and products. And their biggest customer will be the government. Hence, an initial sale (quick money) will lead to years of high expenses for the government when it has to buy back the derived data products and services … costing many times more than was earned in the initial data sales.
In contrast, if the data is free for commercial use, then (a) the industry can only charge for the added value, (b) many more business cases suddenly make sense and even smaller added-value service or product can be offered, and (c) existing data is exploited for all decisions it is needed, (d) other agencies, universities, and private companies can focus on collecting complementing (instead of duplicate) data, …
To summarize: Opening data for free (or cheap) online access is seen as more cost-effective in the long run. It also allows the government to make better decisions, provide the citizens better service, and assures their national geospatial data holdings are exploited to the fullest …
Here some further reading on this topic by MetroGIS based on ideas of the NSGIC (National States Geographic Information Council) who is committed to efficient government through adoption of geospatial information by making “… all non-sensitive geospatial data, produced or maintained using taxpayer funds, a part of the public record.”

Keeping ESRI Honest

Friends of LASzip and LAZ, it has come to my attention (from more than one source) that a certain company East of LA has started to more aggressively promote their proprietary LiDAR format known as the “LAZ clone” (more hereherehere and here but also read the comments) by approaching individual stake holders of the LiDAR community. Trying to convince others to drop support of the open source LASzip compression for LiDAR management, server, and storage in favour of the proprietary “LAZ clone”, said company resorts – if needed – to bad-mouthing the LAZ format with made up arguments that attack the suitability of LASzip for “professional” use.

It appears the LiDAR lock-in PR campaign is now in full swing despite of my repeated attempts to reach out to said company for creating a joint LAS 1.4 compressor that avoids fragmenting the LiDAR data market. And I may just be hearing back about a tiny tip of the drink-the-koolaid iceberg PR. This seems to mark the start of the “clone wars” that we at rapidlasso tried to avoid … (-;

laz_clonesOne way to engage the Empire is to draw them out from the darkness into full technical transparency. After all, the only argument for why the “LAZ clone” had to be created was that LAZ lacks a “particular feature” for efficient employment in the cloud, yet the question what this “particular feature” was has always been dodged (because we would have happily added it to LAZ).

How can you help?

  1. Tell us about any “LAZ clone” campaigs you hear about. Especially those targetting stake holders.
  2. Attend LiDAR training events and Webinars by ESRI that may be “teaching” the unsuspecting newbies to lock their LiDAR into the zLAS format as part of a “smart management strategy” and
    – record any arguments made for the “LAZ clone” or against LAZ so we can publically defute them
    – keep them honest by making your unwillingness to drink the koolaid on zLAS known as early as possible.
    – ask tough questions and disrupt with a strong technical critique on the user-unfriendly and vendor lock-in nature of a closed proprietary format soon as they “teach” the LAS to zLAS conversion
  3. Educate folks about the difference between open LAZ and propietary zLAS.

You may comment below (also anonymously) or email us at ‘honestesri@rapidlasso.com‘ if you have any (juicy?) details about “LAZ clone” campaigns or on video or live training courses where it was “taught” to lock-up the LiDAR into zLAS during an initial “optimization” step …