Scrutinizing LiDAR Data from Leica’s Single Photon Scanner SPL100 (aka SPL99)

We show how simple reordering and clever remapping of single photon LiDAR data can reduce file size by a whopping 50%. We also show that there is at least one Leica’s SPL100 sensor out there that should be called SPL99 because one of its 100 beamlets (the one with beamlet ID 53) does not seem to produce any data … (-:

Closeup on the returns of two beamlet shots colored by beamlet ID from 1 (blue) to 100 (red). Beamlet ID 53 is missing.

Following up on a recent discussion in the LAStools user forum we take a closer look at some of the single photon LiDAR collected with Leica’s SPL100 sensor made available as open data by the USGS in form of LASzip-compressed tiles in LAS 1.4 format of point type 6. This investigation was sparked by the curiosity of what value was stored to the “scanner channel” field that was added to the new point types 6 to 10 in the LAS 1.4 specification.

lasview -i USGS_LPC_SD_MORiver_Woolpert_B1_2016_14TNP120310_LAS_2018.laz ^
        -copy_scanner_channel_into_point_source ^
        -color_by_flightline

Visualizing this 2 bit number whose value can range from 0 to 3 for the first tile we downloaded resulted in this non-conclusive “magic eye” visualization. What do you see? A sailboat?

Visualizing the “scanner channel” field by mapping its four different values to different colors.

Jason Stoker from the USGS suggested that this is the truncated “beamlet” ID. Leica’s SPL100 sensor uses 100 beamlets rather than one or two laser beams to collect data. Storing the beamlet IDs between 1 and 100 to this 2 bit field that can only hold numbers between 0 and 3 is kind of pointless and should be avoided. LASzip switches prediction contexts based on this field resulting in slower compression speed and lower compression rates. The beamlet ID is also stored in the 8 bit “user data” field, so that we can simply zero the “scanner channel” field. To investigate this further we downloaded these nine tiles from this FTP site of the USGS:

Whenever we download LAZ files we first run laszip with the ‘-check’ option which performs a sanity check to make sure that the files are not truncated or otherwise corrupted. In our case we get nine solid reports of SUCCESS.

laszip -i USGS_LPC_SD_MORiver_Woolpert_B1_*_2018.laz -check

A visual inspection with lasview tells us that there are a number of flightlines in the data.

lasview -i USGS_LPC_SD_MORiver_Woolpert_B1_*_2018.laz ^
        -points 15000000 ^
        -color_by_flightline

We use las2las to extract flightline 2003 and lasinfo to produce a histogram of GPS times which we use in turn to decide on which quarter second of GPS time worth of data we want to extract again with las2las.

las2las -i USGS_LPC_SD_MORiver_Woolpert_B1_2016_*_LAS_2018.laz ^
        -merged ^
        -keep_point_source 2003 ^
        -o USGS_LPC_SD_MORiver_Woolpert_B1_ps_2002.laz

lasinfo -i USGS_LPC_SD_MORiver_Woolpert_B1_ps_2002.laz ^
        -cd ^
        -histo gps_time 1 ^
        -odix _info -otxt

las2las -i USGS_LPC_SD_MORiver_Woolpert_B1_ps_2002.laz ^
        -keep_gps_time 176475495 176475495.25 ^
        -o USGS_LPC_SD_MORiver_Woolpert_B1_gps176475495_quarter.laz

It always helps to give your LAZ files meaningful names in case you find them again two years later or so. We can nicely see the circular scanning pattern Leica’s SPL100 sensor. With lasview we measure that this single flightline has an extent of about 2000 meters on the ground. The lasinfo report shows a pulse density of around 19 last returns per square meter. We then sort the points by GPS time using lassort. This groups together all the returns that are the result of one “shot” of the laser with 100 beamlets as we can nicely see in the las2txt output below. Each group of returns has slightly below 100 points and there is one group every 0.00002 seconds. This means the SPL100 is firing once every 20 microseconds.

lassort -i USGS_LPC_SD_MORiver_Woolpert_B1_gps176475495_quarter.laz ^
        -gps_time ^
        -odix _sorted -olaz

las2txt -i USGS_LPC_SD_MORiver_Woolpert_B1_gps176475495_quarter_sorted.laz ^
        -parse tuxyz ^
        -stdout | more
176475495.000008 4 514408.78 4830989.78 487.79
176475495.000008 9 514410.38 4830987.49 487.70
176475495.000008 47 514411.49 4830987.71 487.70
        [ ... 86 lines deleted ... ]
176475495.000008 39 514408.53 4830991.81 487.80
176475495.000008 50 514407.97 4830991.69 487.80
176475495.000008 16 514409.24 4830991.46 487.85
176475495.000028 55 514413.51 4830985.79 487.61
176475495.000028 97 514411.10 4830990.03 487.74
176475495.000028 72 514411.30 4830989.53 487.74
        [ ... 82 lines deleted ... ]
176475495.000028 45 514410.30 4830986.19 487.70
176475495.000028 3 514409.15 4830987.52 487.73
176475495.000028 96 514411.81 4830985.46 487.67
176475495.000048 66 514411.35 4830985.15 487.67
176475495.000048 83 514411.59 4830984.65 487.61
176475495.000048 64 514413.09 4830983.93 487.61
        [ ... 78 lines deleted ... ]
176475495.000048 4 514407.30 4830984.82 487.70
176475495.000048 34 514408.65 4830983.01 487.70
176475495.000048 21 514408.11 4830982.90 487.70
176475495.000068 13 514408.25 4830981.13 487.66
176475495.000068 92 514410.53 4830984.23 487.68
176475495.000068 44 514407.17 4830980.88 487.67
        [ ... 80 lines deleted ... ]
176475495.000068 76 514408.67 4830984.37 487.71
176475495.000068 47 514409.23 4830980.27 487.67
176475495.000068 87 514412.11 4830981.93 487.61
176475495.000088 97 514408.80 4830982.62 487.70
176475495.000088 33 514407.24 4830980.68 487.64
176475495.000088 30 514407.36 4830981.77 487.68
[ ... ]

Now we can “play back” the returns in acquisition order. We map returns from one group to the same color in lasview with the new ‘-bin_gps_time_into_point_source 0.00002’ option (that will be available in the next LAStools release). For a slower playback we add ‘-steps 5000’. Press the ‘c’ key to switch through the coloring options. Press the ‘s’ key repeatedly to incrementally show the points. To take a step back press <SHIFT>+’s’.

lasview -i USGS_LPC_SD_MORiver_Woolpert_B1_gps176475495_quarter_sorted.laz ^
        -bin_gps_time_into_point_source 0.00002 ^
        -scale_user_data 2.5 ^
        -steps 5000 ^
        -win 1024 384

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The last image colors the points by the values in the user data field (multiplied by 2.5), which essentially maps the beamlet IDs between 1 and 100 to a rainbow color ramp from blue to red. This tells us how the numbering of the beamlets from 1 to 100 corresponds to their layout in space. The next sequence of images takes a closer look at that.

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From a compression point of view it makes sense to (1) zero the meaningless scanner channel, (2) order the points by GPS time stamps to groups beamlet returns together, and (3) order the points with the same time stamp by the user data field. The compression gain is enormous with the 9 tiles going from over 3 GB to under 2 GB:

ORIGINAL:
337,156,981 USGS_LPC_SD_MORiver_Woolpert_B1_2016_14TNP120300_LAS_2018.laz
331,801,150 USGS_LPC_SD_MORiver_Woolpert_B1_2016_14TNP120310_LAS_2018.laz
358,928,274 USGS_LPC_SD_MORiver_Woolpert_B1_2016_14TNP120320_LAS_2018.laz
328,597,628 USGS_LPC_SD_MORiver_Woolpert_B1_2016_14TNP130300_LAS_2018.laz
355,997,013 USGS_LPC_SD_MORiver_Woolpert_B1_2016_14TNP130310_LAS_2018.laz
360,403,079 USGS_LPC_SD_MORiver_Woolpert_B1_2016_14TNP130320_LAS_2018.laz
355,399,781 USGS_LPC_SD_MORiver_Woolpert_B1_2016_14TNP140300_LAS_2018.laz
354,523,659 USGS_LPC_SD_MORiver_Woolpert_B1_2016_14TNP140310_LAS_2018.laz
357,248,968 USGS_LPC_SD_MORiver_Woolpert_B1_2016_14TNP140320_LAS_2018.laz
  3,140,056,533 Bytes

IMPROVED:
197,641,087 USGS_LPC_SD_MORiver_Woolpert_B1_2016_14TNP120300_LAS_2018_sorted.laz
194,750,096 USGS_LPC_SD_MORiver_Woolpert_B1_2016_14TNP120310_LAS_2018_sorted.laz
210,013,408 USGS_LPC_SD_MORiver_Woolpert_B1_2016_14TNP120320_LAS_2018_sorted.laz
190,687,275 USGS_LPC_SD_MORiver_Woolpert_B1_2016_14TNP130300_LAS_2018_sorted.laz
206,447,730 USGS_LPC_SD_MORiver_Woolpert_B1_2016_14TNP130310_LAS_2018_sorted.laz
209,580,551 USGS_LPC_SD_MORiver_Woolpert_B1_2016_14TNP130320_LAS_2018_sorted.laz
205,827,197 USGS_LPC_SD_MORiver_Woolpert_B1_2016_14TNP140300_LAS_2018_sorted.laz
203,808,113 USGS_LPC_SD_MORiver_Woolpert_B1_2016_14TNP140310_LAS_2018_sorted.laz
206,789,959 USGS_LPC_SD_MORiver_Woolpert_B1_2016_14TNP140320_LAS_2018_sorted.laz
  1,825,545,416 Bytes

Enumerating the 100 beamlets with a geometrically more coherent order would improve compression even more. Can anyone convince Leica to do this? The simple mapping of beamlet IDs shown below that arranges the beamlets into a zigzag order another huge compression gain of 15 percent. Altogether reordering and remapping lower the compressed file size by a whopping 50 percent.

Beamlet ID mapping table to improve spatial coherence.

168,876,666 USGS_LPC_SD_MORiver_Woolpert_B1_2016_14TNP120300_LAS_2018_mapped_sorted.laz
165,241,508 USGS_LPC_SD_MORiver_Woolpert_B1_2016_14TNP120310_LAS_2018_mapped_sorted.laz
176,524,959 USGS_LPC_SD_MORiver_Woolpert_B1_2016_14TNP120320_LAS_2018_mapped_sorted.laz
163,679,216 USGS_LPC_SD_MORiver_Woolpert_B1_2016_14TNP130300_LAS_2018_mapped_sorted.laz
176,086,559 USGS_LPC_SD_MORiver_Woolpert_B1_2016_14TNP130310_LAS_2018_mapped_sorted.laz
178,909,108 USGS_LPC_SD_MORiver_Woolpert_B1_2016_14TNP130320_LAS_2018_mapped_sorted.laz
174,735,634 USGS_LPC_SD_MORiver_Woolpert_B1_2016_14TNP140300_LAS_2018_mapped_sorted.laz
171,679,105 USGS_LPC_SD_MORiver_Woolpert_B1_2016_14TNP140310_LAS_2018_mapped_sorted.laz
174,997,090 USGS_LPC_SD_MORiver_Woolpert_B1_2016_14TNP140320_LAS_2018_mapped_sorted.laz
  1,550,729,845 Bytes

Once this is done a final space-filling sort into a Hilbert-curve or a Morton-order with lassort or lasoptimize would improve spatial coherence for efficient spatial indexing with lasindex.

Oh yes … the SPL100 was not firing on all cylinders. The beamlet ID 53 that would have mapped to 61 in our table was not present in any of the 9 tiles with 355,047,478 points that we had downloaded as the lasinfo histogram below shows.

lasinfo -i USGS_LPC_SD_MORiver_Woolpert_B1_2016_*_2018.laz -merged -histo user_data 1
lasinfo (180911) report for 9 merged files
reporting all LAS header entries:
  file signature:             'LASF'
  file source ID:             0
  global_encoding:            17
  project ID GUID data 1-4:   194774FA-35FE-4591-D484-010AFA13F6D9
  version major.minor:        1.4
  system identifier:          'Woolpert LAS'
  generating software:        'GeoCue LAS Updater'
  file creation day/year:     332/2017
  header size:                375
  offset to point data:       1376
  number var. length records: 1
  point data format:          6
  point data record length:   30
  number of point records:    0
  number of points by return: 0 0 0 0 0
  scale factor x y z:         0.01 0.01 0.01
  offset x y z:               0 0 0
  min x y z:                  512000.00 4830000.00 286.43
  max x y z:                  514999.99 4832999.99 866.81
  start of waveform data packet record: 0
  start of first extended variable length record: 0
  number of extended_variable length records: 0
  extended number of point records: 355047478
  extended number of points by return: 298476060 52480771 3929583 157365 3699 0 0 0 0 0 0 0 0 0 0
variable length header record 1 of 1:
  reserved             0
  user ID              'LASF_Projection'
  record ID            2112
  length after header  943
  description          'OGC WKT Coordinate System'
    WKT OGC COORDINATE SYSTEM:
    COMPD_CS["NAD83(2011) / UTM zone 14N + NAVD88 height - Geoid12B (metre)",PROJCS["NAD83(2011) / UTM zone 14N",GEOGCS["NAD83(2011)",DATUM["NAD83_National_Spat
ial_Reference_System_2011",SPHEROID["GRS 1980",6378137,298.257222101,AUTHORITY["EPSG","7019"]],AUTHORITY["EPSG","1116"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","
8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AUTHORITY["EPSG","6318"]],PROJECTION["Transverse_Mercator"],PARAMETER["latitude_of_origin",0]
,PARAMETER["central_meridian",-99],PARAMETER["scale_factor",0.9996],PARAMETER["false_easting",500000],PARAMETER["false_northing",0],UNIT["metre",1,AUTHORITY["EP
SG","9001"]],AXIS["Easting",EAST],AXIS["Northing",NORTH],AUTHORITY["EPSG","6343"]],VERT_CS["NAVD88 height - Geoid12B (metre)",VERT_DATUM["North American Vertica
l Datum 1988",2005,AUTHORITY["EPSG","5103"]],UNIT["metre",1,AUTHORITY["EPSG","9001"]],AXIS["Gravity-related height",UP],AUTHORITY["EPSG","5703"]]]
the header is followed by 4 user-defined bytes
LASzip compression (version 3.1r0 c3 50000): POINT14 3
reporting minimum and maximum for all LAS point record entries ...
  X            51200000   51499999
  Y           483000000  483299999
  Z               28643      86681
  intensity        3139      12341
  return_number       1          5
  number_of_returns   1          5
  edge_of_flight_line 0          0
  scan_direction_flag 0          1
  classification      1         10
  scan_angle_rank  -127        127
  user_data           1        100
  point_source_ID  1061       2005
  gps_time 176475467.194000 176496233.636563
  extended_return_number          1      5
  extended_number_of_returns      1      5
  extended_classification         1     10
  extended_scan_angle        -21167  21167
  extended_scanner_channel        0      3
number of first returns:        298476060
number of intermediate returns: 6282
number of last returns:         355000765
number of single returns:       298435629
overview over extended number of returns of given pulse: 298435629 52515017 3935373 157750 3709 0 0 0 0 0 0 0 0 0 0
histogram of classification of points:
       138382030  unclassified (1)
       207116732  ground (2)
         9233160  noise (7)
          310324  water (9)
            5232  rail (10)
 +-> flagged as withheld:  9233160
 +-> flagged as extended overlap: 226520346
user data histogram with bin size 1.000000
  bin 1 has 3448849
  bin 2 has 3468566
  bin 3 has 3721848
  bin 4 has 3376990
  bin 5 has 3757996
  bin 6 has 3479546
  bin 7 has 3799930
  bin 8 has 3766887
  bin 9 has 3448383
  bin 10 has 3966036
  bin 11 has 3232086
  bin 12 has 3686789
  bin 13 has 3763869
  bin 14 has 3847765
  bin 15 has 3659059
  bin 16 has 3666918
  bin 17 has 3427468
  bin 18 has 3375320
  bin 19 has 3222116
  bin 20 has 3598643
  bin 21 has 3108323
  bin 22 has 3553625
  bin 23 has 3782185
  bin 24 has 3577792
  bin 25 has 3063871
  bin 26 has 3451800
  bin 27 has 3518763
  bin 28 has 3845852
  bin 29 has 3366980
  bin 30 has 3797986
  bin 31 has 3623477
  bin 32 has 3606798
  bin 33 has 3762737
  bin 34 has 3861023
  bin 35 has 3821228
  bin 36 has 3738173
  bin 37 has 3902190
  bin 38 has 3726752
  bin 39 has 3910989
  bin 40 has 3771132
  bin 41 has 3718437
  bin 42 has 3609113
  bin 43 has 3339941
  bin 44 has 3003191
  bin 45 has 3697140
  bin 46 has 2329171
  bin 47 has 3398836
  bin 48 has 3511882
  bin 49 has 3719592
  bin 50 has 2995275
  bin 51 has 3673925
  bin 52 has 3535992
  bin 54 has 3799430
  bin 55 has 3613345
  bin 56 has 3761436
  bin 57 has 3296831
  bin 58 has 3810146
  bin 59 has 3768464
  bin 60 has 3520871
  bin 61 has 3833149
  bin 62 has 3639778
  bin 63 has 3623008
  bin 64 has 3581480
  bin 65 has 3663180
  bin 66 has 3661434
  bin 67 has 3684374
  bin 68 has 3723125
  bin 69 has 3552397
  bin 70 has 3554207
  bin 71 has 3535494
  bin 72 has 3621334
  bin 73 has 3633928
  bin 74 has 3631845
  bin 75 has 3526502
  bin 76 has 3605631
  bin 77 has 3452006
  bin 78 has 3796382
  bin 79 has 3731841
  bin 80 has 3683314
  bin 81 has 3806024
  bin 82 has 3749709
  bin 83 has 3808218
  bin 84 has 3634032
  bin 85 has 3631015
  bin 86 has 3712206
  bin 87 has 3627775
  bin 88 has 3674966
  bin 89 has 3231151
  bin 90 has 3780037
  bin 91 has 3621958
  bin 92 has 3623264
  bin 93 has 3853536
  bin 94 has 3623380
  bin 95 has 3418309
  bin 96 has 3374827
  bin 97 has 3464734
  bin 98 has 3562560
  bin 99 has 3078686
  bin 100 has 3426924

 

Estonia leads in Open LiDAR: nationwide & multi-temporal Point Clouds now Online

At the beginning of July 2018 the Baltic country of Estonia – with an area of 45 thousand square kilometers inhabited by around 1.3 million people – opened much of their geospatial data archives and is now offering easy and free download of LiDAR point clouds nationwide via a portal of the Estonian Land Board. What is even more exciting is that multi-temporal data sets flown in different years and seasons are available. Raw LiDAR point clouds collected either during a “regular flight in spring” or during a “forestry flight in summer” can be obtained for multiple years. The 1 km by 1 km tile with map sheet index 377650, for example, is available for four different LiDAR surveys carried out in spring 2011, summer 2013, spring 2015 and summer 2017. This offers incredible potential for studying temporal changes of man-made or natural environments. The screenshot sequence below shows how to navigate to the download site starting from this page.

We found out about this open data release during our hands-on workshop on LiDAR and photogrammetry point cloud processing with LAStools that was part of the UAV remote sensing summer school in Tartu, Estonia. See our calendar for upcoming events or contact us for holding a similar event at your university, agency, company, or conference. 

The LiDAR data provided on the download portal is compressed with LASzip and provided as 1km by 1km LiDAR tiles in LAZ format. You can search for these tiles via their Estonian 1:2000 map sheet indices. To find out which map sheet index corresponds to the tile you are interested in you can overlay the maps sheet indices over an online map. However, you will need to zoom in before you can see the indices as illustrated in the screenshot sequence below. Here a zoom to the map sheet indices for the area that we visited during the social event of the summer school.

One thing we noticed is that the tiles contain only a single layer of points. The overlaps between flightlines were removed which results in a more uniform point density but strips the user of the possibility to do their own flightline alignment checks with lasoverlap. Below you see the spring 2014 acquisition for the tile with map sheet index 475861 colored by classification, elevation, return type, flightline ID and intensity.

The license for the open data of the Estonian Land Board is very permissive and can be found here. Agreeing to the licence gives the licence holder the rights to use data free of charge for an unspecified term, to good purpose in accordance with law and best practice. Licence holder may produce derivatives of data, combine data with its own products or services, use data for commercial or non-commercial purposes and redistribute data. The licence holder obliges to refer to the origin of data when publishing and redistributing data. The reference must include the name of the licensor, the title of data, the age of data (or the date of data extraction).

Complete LiDAR Processing Pipeline: from raw Flightlines to final Products

This tutorial serves as an example for a complete end-to-end workflow that starts with raw LiDAR flightlines (as they may be delivered by a vendor) to final classified LiDAR tiles and derived products such as raster DTM, DSM, and SHP files with contours, building footprint and vegetation layers. The three example flightlines we are using here were flown in Ayutthaya, Thailand with a RIEGL LMS Q680i LiDAR scanner by Asian Aerospace Services who are based at the Don Mueang airport in Bangkok from where they are serving South-East-Asia and beyond. You can download them here:

Quality Checking

The minimal quality checks consist of generating textual reports (lasinfo & lasvalidate), inspecting the data visually (lasview), making sure alignment and overlap between flightlines fulfill expectations (lasoverlap), and measuring pulse density per square meter (lasgrid). Additional checks for points replication (lasduplicate), completeness of all returns per pulse (lasreturn), and validation against external ground control points (lascontrol) may also be performed.

lasinfo -i Ayutthaya\strips_raw\*.laz ^
        -cd ^
        -histo z 5 ^
        -histo intensity 64 ^
        -odir Ayutthaya\quality -odix _info -otxt ^
        -cores 3

lasvalidate -i Ayutthaya\strips_raw\*.laz ^
            -o Ayutthaya\quality\validate.xml

The lasinfo report generated with the command line shown computes the average density for each flightline and also generates two histograms, one for the z coordinate with a bin size of 5 meter and one for the intensity with a bin size of 64. The resulting textual descriptions are output into the specified quality folder with an appendix ‘_info’ added to the original file name. Perusing these reports tells you that there are up to 7 returns per pulse, that the average pulse density per flightline is between 7.1 to 7.9 shots per square meter, that the point source IDs of the points are already populated correctly, that there are isolated points far above and far below the scanned area, and that the intensity values range from 0 to 1023 with the majority being below 400. The warnings in the lasinfo and the lasvalidate reports about the presence of return numbers 6 and 7 have to do with the history of the LAS format and can safely be ignored.

lasoverlap -i Ayutthaya\strips_raw\*.laz ^
           -files_are_flightlines ^
           -min_diff 0.1 -max_diff 0.3 ^
           -odir Ayutthaya\quality -o overlap.png

This results in two color illustrations. One image shows the flightline overlap with blue indicating one flightline, turquoise indicating two, and yellow indicating three flightlines. Note that wet areas (rivers, lakes, rice paddies, …) without LiDAR returns affect this visualization. The other image shows how well overlapping flightlines align. Their vertical difference is color coded with while meaning less than 10 cm error while saturated red and blue indicate areas with more than 30 cm positive or negative difference.

One pixel wide red and blue along building edges and speckles of red and blue in vegetated areas are normal. We need to look-out for large systematic errors where terrain features or flightline outlines become visible. If you click yourself through this photo album you will eventually see typical examples (make sure to read the comments too). One area slightly below the center looks suspicious. We load the PNG into the GUI to pick this area for closer inspection with lasview.

lasview -i Ayutthaya\strips_raw\*.laz -gui

Why these flightline differences exist and whether they are detrimental to your purpose are questions that you will have to explore further. For out purpose this isolated difference was noted but will not prevent us from proceeding further. Next we want to investigate the pulse density. We do this with lasgrid. We know that the average last return density per flightline is between 7.1 to 7.9 shots per square meter. We set up the false color map for lasgrid such that it is blue when the last return density drops to 5 shots (or less) per square meter and such that it is red when the last return density reaches 10 shots (or more).

lasgrid -i Ayutthaya\strips_raw\*.laz -merged ^
        -keep_last ^
        -step 2 -density ^
        -false -set_min_max 4 8 ^
        -odir Ayutthaya\quality -o density_4ppm_8ppm.png

The last return density per square meter mapped to a color: blue is 5 or less, red is 10 or more.

The last return density image clearly shows how the density increases to over 10 pulses per square meter in all areas of flightline overlap. However, as there are large parts covered by only one flightline their density is the one that should be considered. We note great variations in density along the flightlines. Those have to do with aircraft movement and in particular with the pitch. When the nose of the plane goes up even slightly, the gigantic “fan of laser pulses” (that can be thought of as being rigidly attached at the bottom perpendicular to the aircraft flight direction) is moving faster forward on the ground far below and therefore covers it with fewer shots per square meter. Conversely when the nose of the plane goes down the spacing between scan lines far below the plane are condensed so that the density increases. Inclement weather and the resulting airplane pitch turbulence can have a big impact on how regular the laser pulse spacing is on the ground. Read this article for more on LiDAR pulse density and spacing.

LiDAR Preparation

When you have airborne LiDAR in flightlines the first step is to tile the data into square tiles that are typically 1000 by 1000 or – for higher density surveys – 500 by 500 meters in size. This can be done with lastile. We also add a buffer of 30 meters to each tile. Why buffers are important for tile-based processing is explained here. We choose 30 meters as this is larger than any building we expect in this area and slightly larger than the ‘-step’ size we use later when classifying the points into ground and non-ground points with lasground.

lastile -i Ayutthaya\strips_raw\*.laz ^
        -tile_size 500 -buffer 30 -flag_as_withheld ^
        -odir Ayutthaya\tiles_raw -o ayu.laz

NOTE: Usually you will have to add ‘-files_are_flightlines’ or ‘-apply_file_source_ID’ to the lastile command shown above in order to preserve the information which points is from which flightline. We do not have to do this here as evident from the lasinfo reports we generated earlier. Not only is the file source ID in the LAS header is correctly set to 2, 3, or 4 reflecting the intended flightline numbering evident from the file names. Also the point source ID of each point is already set to the correct value 2, 3, or 4. For more info see this or this discussion from the LAStools user forum.

Next we classify isolated points that are far from most other points with lasnoise into the (default) classification code 7. See the README file for the meaning of the parameters and play around with different setting to get a feel for how to make this process more or less aggressive.

lasnoise -i Ayutthaya\tiles_raw\ayu*.laz ^
         -step_xy 4 -step_z 2 -isolated 5 ^
         -odir Ayutthaya\tiles_denoised -olaz ^
         -cores 4

Especially for ground classification it is important that low noise points are excluded. You should inspect a number of tiles with lasview to make sure the low noise are all pink now if you color them by classification.

lasview -i Ayutthaya\tiles_denoised\ayu*.laz -gui

While the algorithms in lasground are designed to withstand a few noise points below the ground, you will find that it will include them into the ground model if there are too many of them. Hence, it is important to tell lasground to ignore these noise points. For the other parameters used see the README file of lasground.

lasground -i Ayutthaya\tiles_denoised\ayu*.laz ^
          -ignore_class 7 ^
          -city -ultra_fine ^
          -compute_height ^
          -odir Ayutthaya\tiles_ground -olaz ^
          -cores 4

You should visually check the resulting ground classification of each tile with lasview by selecting smaller subsets (press ‘x’, draw a rectangle, press ‘x’ again, use arrow keys to walk) and then switch back and forth between a triangulation of the ground points (press ‘g’ and then press ‘t’) and a triangulation of last returns (press ‘l’ and then press ‘t’). See the README of lasview for more information on those hotkeys.

lasview -i Ayutthaya\tiles_ground\ayu*.laz -gui

This way I found at least one tile that should be reclassified with ‘-metro’ instead of ‘-city’ because it still contained one large building in the ground classification. Alternatively you can correct miss-classifications manually using lasview as shown in the next few screen shots.

This is an optional step for advanced users who have a license. In case you managed to do such a manual edit and saved it as a LAY file using LASlayers (see the README file of lasview) you can overwrite the old file with:

laslayers -i Ayutthaya\tiles_ground\ayu_669500_1586500.laz -ilay ^
          -o Ayutthaya\tiles_ground\ayu_669500_1586500_edited.laz

move Ayutthaya\tiles_ground\ayu_669500_1586500_edit.laz ^
     Ayutthaya\tiles_ground\ayu_669500_1586500.laz

The next step classifies those points that are neither ground (2) nor noise (7) into building (or rather roof) points (class 6) and high vegetation points (class 5). For this we use lasclassify with the default parameters that only considers points that are at least 2 meters above the classified ground points (see the README for details on all available parameters).

lasclassify -i Ayutthaya\tiles_ground\ayu*.laz ^
            -ignore_class 7 ^
            -odir Ayutthaya\tiles_classified -olaz ^
            -cores 4

We  check the classification of each tile with lasview by selecting smaller subsets (press ‘x’, draw a rectangle, press ‘x’ again) and by traversing with the arrow keys though the point cloud. You will find a number of miss-classifications. Boats are classified as buildings, water towers or complex temple roofs as vegetation, … and so on. You could use lasview to manually correct any classifications that are really important.

lasview -i Ayutthaya\tiles_classified\ayu*.laz -gui

Before delivering the classified LiDAR tiles to a customer or another user it is imperative to remove the buffers that were carried through all computations to avoid artifacts along the tile boundary. This can also be done with lastile.

lastile -i Ayutthaya\tiles_classified\ayu*.laz ^
        -remove_buffer ^
        -odir Ayutthaya\tiles_final -olaz ^
        -cores 4

Together with the tiling you may want to deliver a tile overview file in KML format (or in SHP format) that you can easily generate with lasboundary using this command line:

lasboundary -i Ayutthaya\tiles_final\ayu*.laz ^
            -use_bb ^
            -overview -labels ^
            -o Ayutthaya\tiles_overview.kml

The small KML file generated b lasboundary provides a quick overview where tiles are located, their names, their bounding box, and the number of points they contain.

Derivative production

The most common derivative product produced from LiDAR data is a Digital Terrain Model (DTM) in form of an elevation raster. This can be obtained by interpolating the ground points with a triangulation (i.e. a Delaunay TIN) and by sampling the TIN at the center of each raster cell. The pulse density of well over 4 shots per square meter definitely supports a resolution of 0.5 meter for the raster DTM. From the ground-classified tiles with buffer we compute the DTM using las2dem as follows:

las2dem -i Ayutthaya\tiles_ground\ayu*.laz ^
        -keep_class 2 ^
        -step 0.5 -use_tile_bb ^
        -odir Ayutthaya\tiles_dtm -obil ^
        -cores 4

It’s important to add ‘-use_tile_bb’ to the command line which limits the raster generation to the original tile sizes of 500 by 500 meters in order not to rasterize the buffers that are extending the tiles 30 meters in each direction. We used the BIL format so that we inspect the resulting elevation rasters with lasview:

lasview -i Ayutthaya\tiles_dtm\ayu*.bil -gui

To create a hillshaded version of the DTM you can use your favorite raster processing package such as GDAL or GRASS but you could also use the BLAST extension of LAStools and create a large seamless image with blast2dem as follows:

blast2dem -i Ayutthaya\tiles_dtm\ayu*.bil -merged ^
          -step 0.5 -hillshade -epsg 32647 ^
          -o Ayutthaya\dtm_hillshade.png

Because blast2dem does not parse the PRJ files that accompany the BIL rasters we have to specify the EPSG code explicitly to also get a KML file that allows us to visualize the LiDAR in Google Earth.

A a hillshading of the merged DTM rasters produced with blast2dem.

Next we generate a Digital Surface Model (DSM) that includes the highest objects that the laser has hit. We use the spike-free algorithm that is implemented in las2dem that creates a triangulation of the highest returns as follows:

las2dem -i Ayutthaya\tiles_denoised\ayu*.laz ^
        -drop_class 7 ^
        -step 0.5 -spike_free 1.2 -use_tile_bb ^
        -odir Ayutthaya\tiles_dsm -obil ^
        -cores 4

We used 1.0 as the freeze value for the spike free algorithm because this is about three times the average last return spacing reported in the individual lasinfo reports generated during quality checking. Again we inspect the resulting rasters with lasview:

lasview -i Ayutthaya\tiles_dsm\ayu*.bil -gui

For reason of comparison we also generate the DSM rasters using a simple first-return interpolation again with las2dem as follows:

las2dem -i Ayutthaya\tiles_denoised\ayu*.laz ^
        -drop_class 7 -keep_first ^
        -step 0.5 -use_tile_bb ^
        -odir Ayutthaya\tiles_dsm -obil ^
        -cores 4

A few direct side-by-side comparison between a spike-free DSM and a first-return DSM shows the difference that are especially noticeable along building edges and in large trees.

Another product that we can easily create are building footprints from the automatically classified roofs using lasboundary. Because the tool is quite scalable we can simply on-the-fly merge the final tiles. This also avoids including duplicate points from the tile buffer whose classifications are also often less accurate.

lasboundary -i Ayutthaya\tiles_final\ayu*.laz -merged ^
            -keep_class 6 ^
            -disjoint -concavity 1.1 ^
            -o Ayutthaya\buildings.shp

Similarly we can use lasboundary to create a vegetation layer from those points that were automatically classified as high vegetation.

lasboundary -i Ayutthaya\tiles_final\ayu*.laz -merged ^
             -keep_class 5 ^
             -disjoint -concavity 3 ^
             -o Ayutthaya\vegetation.shp

We can also produce 1.0 meter contour lines from the ground classified points. However, for nicer contours it is beneficial to first generate a subset of the ground points with lasthin using option ‘-contours 1.0’ as follows:

lasthin -i Ayutthaya\tiles_final\ayu*.laz ^
        -keep_class 2 ^
        -step 1.0 -contours 1.0 ^
        -odir Ayutthaya\tiles_temp -olaz ^
        -cores 4

We then merge all subsets of ground points from those temporary tiles on-the-fly into one (using the ‘-merged’ option) and let blast2iso from the BLAST extension of LAStools generate smoothed and simplified 1 meter contours as follows:

blast2iso -i Ayutthaya\tiles_temp\ayu*.laz -merged ^
          -iso_every 1.0 ^
          -smooth 2 -simplify_length 0.5 -simplify_area 0.5 -clean 5.0 ^
          -o Ayutthaya\contours_1m.shp

Finally we composite all of our derived LiDAR products into one map using QGIS and then fuse it with data from OpenStreetMap that we’ve downloaded from BBBike. Here you can download the OSM data that we use.

It’s in particular interesting to compare the building footprints that were automatically derived from our LiDAR processing pipeline with those mapped by OpenStreetMap volunteers. We immediately see that there is a lot of volunteering work left to do and the LiDAR-derived data can assist us in directing those mapping efforts. A closer look reveals the (expected) quality difference between hand-mapped and auto-generated data.

The OSM buildings are simpler. These polygons are drawn and divided into logical units by a human. They are individually verified and correspond to actual buildings. However, they are less aligned with the Google Earth satellite image. The LiDAR-derived buildings footprints are complex because lasboundary has no logic to simplify the complicated polygonal chains that enclose the points that were automatically classified as roof into rectilinear shapes or to break directly adjacent roof points into separate logical units. However, most buildings are found (but also objects that are not buildings) and their geospatial alignment is as good as that of the LiDAR data.

Scotland’s LiDAR goes Open Data (too)

Following the lead of England and Wales, the Scottish LiDAR is now also open data. The implementation of such an open geospatial policy in the United Kingdom was spear-headed by the Environment Agency of England who started to make all of their LiDAR holdings available as open data. In September 2015 they opened DTM and DSM raster derivatives down to 25 cm resolution and in March 2016 also the raw point clouds went online our compressed and open LAZ format (more info here) – all with the very permissible Open Government Licence v3. This treasure cove of geospatial data was collected by the Environment Agency Geomatics own survey aircraft mainly for flood mapping purposes. The data that had been access restricted for the past 17 years of operation and was made open only after it was shown that restricting access in order to recover costs to finance future operations – a common argument for withholding tax-payer funded data – was nothing but an utter myth. This open data policy has resulted in an incredible re-use of the LiDAR and the Environment Agency has literally been propelled into the role of a “champion for open data” inspiring Wales (possibly the German states of North-Rhine Westfalia and Thuringia) and now also Scotland to open up their geospatial archives as well …

Huge LAS files available for download from the Scottish Open Data portal.

We went to the nice online portal of Scotland to download three files from the Phase II LiDAR for Scotland that are provided as uncompressed LAS files, namely LAS_NN45NE.las, LAS_NN55NE.las, and LAS_NN55NW.las, whose sizes are listed as 1.2 GB, 2.8 GB, and 4.7 GB in the screenshot above. Needless to say that it took quite some time and several restarts (using wget with option ‘-c’) to successfully download these very large LAS files.

laszip -i LAS_NN45NE.las -odix _cm -olaz -rescale 0.01 0.01 0.01 
laszip -i LAS_NN45NE.las -odix _mm -olaz
laszip -i LAS_NN55NE.las -odix _cm -olaz -rescale 0.01 0.01 0.01 
laszip -i LAS_NN55NE.las -odix _mm -olaz
laszip -i LAS_NN55NW.las -odix _cm -olaz -rescale 0.01 0.01 0.01 
laszip -i LAS_NN55NW.las -odix _mm -olaz

After downloading we decided to see how well these files compress with LASzip by running the six commands shown above creating LAZ files when re-scaling of coordinate resolution to centimeter (cm) and LAZ files with the original millimeter (mm) coordinate resolution (i.e. the original scale factors are 0.001 which is somewhat excessive for aerial LiDAR where the error in position per coordinate is typically between 5 cm and 20 cm). Below you see the resulting file sizes for the three different files.

 1,164,141,247 LAS_NN45NE.las
   124,351,690 LAS_NN45NE_cm.laz (1 : 9.4)
   146,651,719 LAS_NN45NE_mm.laz (1 : 7.9)
 2,833,123,863 LAS_NN55NE.las
   396,521,115 LAS_NN55NE_cm.laz (1 : 7.1)
   474,767,495 LAS_NN55NE_mm.laz (1 : 6.0)
 4,664,782,671 LAS_NN55NW.las
   531,454,473 LAS_NN55NW_cm.laz (1 : 8.8)
   629,141,151 LAS_NN55NW_mm.laz (1 : 7.4)

The savings in download time and storage space of storing the LiDAR in LAZ versus LAS are sixfold to tenfold. If I was a tax payer in Scotland and if my government was hosting open data on in the Amazon cloud (i.e. paying for AWS cloud services with my taxes) I would encourage them to store their data in a more compressed format. Some more details on the data.

According to the provided meta data, the Scottish Public Sector LiDAR Phase II dataset was commissioned by the Scottish Government in response to the Flood Risk Management Act (2009). The project was managed by Sniffer and the contract was awarded to Fugro BKS. Airborne LiDAR data was collected for 66 sites (the dataset does not have full national coverage) totaling 3,516 km^2 between 29th November 2012 and 18th April 2014. The point density was a minimum of 1 point/sqm, and approximately 2 points/sqm on average. A DTM and DSM were produced from the point clouds, with 1m spatial resolution. The Coordinate reference system is OSGB 1936 / British National Grid (EPSG code 27700). The data is licensed under an Open Government Licence. However, under the use constraints section it now only states that the following attribution statement must be used to acknowledge the source of the information: “Copyright Scottish Government and SEPA (2014)” but also that Fugro retain the commercial copyright, which is somewhat disconcerting and may require more clarification. According to this tweet a lesser license (NCGL) applies to the raw LiDAR point clouds. Below a lasinfo report for the large LAS_NN55NW.las as well as several visualizations with lasview.

lasinfo (170915) report for LAS_NN55NW.las
reporting all LAS header entries:
 file signature: 'LASF'
 file source ID: 0
 global_encoding: 1
 project ID GUID data 1-4: 00000000-0000-0000-0000-000000000000
 version major.minor: 1.2
 system identifier: 'Riegl LMS-Q'
 generating software: 'Fugro LAS Processor'
 file creation day/year: 343/2016
 header size: 227
 offset to point data: 227
 number var. length records: 0
 point data format: 1
 point data record length: 28
 number of point records: 166599373
 number of points by return: 149685204 14102522 2531075 280572 0
 scale factor x y z: 0.001 0.001 0.001
 offset x y z: 250050 755050 270
 min x y z: 250000.000 755000.000 203.731
 max x y z: 254999.999 759999.999 491.901
reporting minimum and maximum for all LAS point record entries ...
 X -50000 4949999
 Y -50000 4949999
 Z -66269 221901
 intensity 39 2046
 return_number 1 4
 number_of_returns 1 4
 edge_of_flight_line 0 1
 scan_direction_flag 1 1
 classification 1 11
 scan_angle_rank -30 30
 user_data 0 3
 point_source_ID 66 91
 gps_time 38230669.389034 38402435.753789
number of first returns: 149685204
number of intermediate returns: 2813604
number of last returns: 149687616
number of single returns: 135599244
overview over number of returns of given pulse: 135599244 23122229 6754118 1123782 0 0 0
histogram of classification of points:
 287819 unclassified (1)
 109019874 ground (2)
 14476880 low vegetation (3)
 3487218 medium vegetation (4)
 39141518 high vegetation (5)
 165340 building (6)
 13508 rail (10)
 7216 road surface (11)

Kudos to the Scottish government for opening their data. We hereby acknowledge the source of the LiDAR that we have used in the experiments above as “Copyright Scottish Government and SEPA (2014)”.

LAStools Win Big at INTERGEO Taking Home Two Innovation Awards

PRESS RELEASE (for immediate release)
October 2, 2017
rapidlasso GmbH, Gilching, Germany

At INTERGEO 2017 in Berlin, rapidlasso GmbH – the makers of the popular LiDAR processing software LAStools – were awarded top honors in both of the categories they had been nominated for: most innovative software and most innovative startup. The third award for most innovative hardware went to Leica Geosystems for the BLK360 terrestrial scanner. The annual Wichman Innovation Awards have been part of INTERGEO for six years now. Already at the inaugural event in 2012 the open source LiDAR compressor LASzip of rapidlasso GmbH had been nominated, coming in as runner-up in second place.

Dr. Martin Isenburg, the founder and CEO of rapidlasso GmbH, receives the two innovation awards at the ceremony during INTERGEO 2017 in Berlin.

After receiving the two awards Dr. Martin Isenburg, the founder and CEO of rapidlasso GmbH, was quick to thank the “fun, active, and dedicated user community” of the LAStools software for their “incredible support in the online voting”. He pointed out that it was its users who make LAStools more than just an efficient software for processing point clouds. Since 2011, the community surrounding LAStools has constantly grown to several thousand users who help and motivate each other in designing workflows and in solving format issues and processing challenges. They are an integral part of what makes these tools so valuable, so Dr. Isenburg.

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 http://rapidlasso.com for more information.

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 … (-: