1. What are your major sources for surface scan point clouds?

2. What formats do you use to store point clouds?

3. What formats do you use to transfer point clouds (both internally and to external entities)?

4. What are your most common use cases for point clouds?

5. What are your main application areas for point clouds?

6. How do you store point clouds?

7. What attributes do your point clouds contain besides XYZ coordinates?

8. What conversion do you apply to the point clouds in order to use them?

9. Which temporal aspect of point clouds are relevant for you?

10. During what phase do you encounter interoperability challenges?

11. What do you consider the most important area of point cloud standardization?

12. What volume of point clouds have you managed/processed/stored/etc. in the last 12 month?

13. What tools do you use?

P.J.M.vanOosterom@tudelft.nl

Airborne LiDAR, Terrestrial Lidar (including Mobile Mapping), Indoor Laser Scanning, SONAR (single and multi-beam echo’s)

LAS (ASPRS), LAZ, ASCII, Oracle SDO_PC, PostgreSQL-PostGIS PCPATCH, PDAL-generated blocks, Flat table

Water Management, Smart City

None, direct use of point clouds

Temporal granularity at data set (a ‘point cloud’) level, Temporal resolution / update frequency years

Storage / Management, Dissemination, Visualization / Interaction

Data Model, File Format / Encoding, DBMS / SQL, Web Service (WxxS) protocol

More than 1 billion (109) points

PDAL, Potree, LAStools, Esri ArcGIS, Oracle SDO_PC, PosgreSQL/PostGIS, MonetDB, FME

looking forward to results!

Airborne LiDAR, Photogrammetry

Visualization, Digital Terrain Modelling, Feature Extraction, GIS

Civil Engineering, Asset Management

In a database, In the cloud

Timestamp, Intensity, Classification

To features (vector object rafter detection/recognition), None, direct use of point clouds

Temporal granularity at data set (a ‘point cloud’) level, Temporal resolution / update frequency months, Temporal resolution / update frequency days, Monitoring applications, change detection

Change Reference System, Analysis / Simluation, Dissemination, Visualization / Interaction

File Format / Encoding, Web Service (WxxS) protocol

More than 1 trillion (10^12) points

Potree, PosgreSQL/PostGIS, environment lidar, qt modeler

morreale.jean.roc@pasdecalais.fr

Conseil Dép. du Pas de Calais

Airborne LiDAR, Photogrammetry

LAS (ASPRS), LAZ, PostgreSQL-PostGIS PCPATCH

Digital Terrain Modelling, GIS

In a file on a network drive, In a database

Timestamp, Colour, Classification

To regular grid (raster), Level of Detail (LoD) representation

Temporal resolution / update frequency years

Combining Data from multiple source, Analysis / Simluation

Data Model, File Format / Encoding

More than 100 million (10^6) points

PDAL, Potree, LAStools, GRASS, PosgreSQL/PostGIS, cloudcompare

matthias_mueller@tu-dresden.de

Airborne LiDAR, Photogrammetry

Digital Terrain Modelling, Feature Extraction, GIS

Smart City, Civil Engineering

In a file on a computer, In a file on a network drive

To regular grid (raster), Level of Detail (LoD) representation

Temporal granularity at data set (a ‘point cloud’) level, Temporal resolution / update frequency years, Temporal resolution / update frequency months, Monitoring applications, change detection

Storage / Management, Combining Data from multiple source, Change Reference System

Data Model, File Format / Encoding

More than 1 billion (10^9) points

LAStools, Esri ArcGIS, PCL

Terrestrial Lidar (including Mobile Mapping)

In a file on a network drive, REMOVABLE DRIVE

None, direct use of point clouds

Temporal granularity at data set (a ‘point cloud’) level

Storage / Management, DISSEMINATION

More than 1 trillion (10^12) points

Airborne LiDAR, Terrestrial Lidar (including Mobile Mapping), Indoor Laser Scanning

LAS (ASPRS), LAZ, ASCII, PLY, Flat table

LAS (ASPRS), E57, ASCII, PLY

Visualization, Digital Terrain Modelling

Timestamp, Intensity, Colour

To TIN, None, direct use of point clouds

Monitoring applications, change detection

Storage / Management, Combining Data from multiple source, Dissemination, Visualization / Interaction

Data Model, Web Service (WxxS) protocol

More than 100 million (10^6) points

Esri ArcGIS, CloudCompare

Used a lot in previous work, mainly for internal and external building mapping using a lidar scanner. Are web services suitable for this because of the large data volumes? Perhaps engineering products or sections on the fly could be interesting.

gene.roe@lidarnews.com

Terrestrial Lidar (including Mobile Mapping)

Visualization, Digital Terrain Modelling, Feature Extraction

Civil Engineering, Asset Management

In a file on a network drive

Intensity, Colour, Classification

To features (vector object rafter detection/recognition)

Monitoring applications, change detection

Storage / Management, Combining Data from multiple source

Data Model, File Format / Encoding

More than 1 billion (10^9) points

Esri ArcGIS, Bentley Pointools, Leica CloudWorx

sbrandsma@cyclomedia.com

Terrestrial Lidar (including Mobile Mapping), Photogrammetry

Visualization, Feature Extraction, Intermediate product for 3D textured mesh reconstruction

Smart City, Asset Management

In a file on a network drive

Timestamp, Colour, Accuracy (stddev)

To features (vector object rafter detection/recognition), None, direct use of point clouds, simplification, 3d delaunay triangulation

Temporal granularity at data set (a ‘point cloud’) level, Temporal resolution / update frequency months

Storage / Management, Combining Data from multiple source, Analysis / Simluation, Visualization / Interaction

Data Model, File Format / Encoding, DBMS / SQL, Web Service (WxxS) protocol

More than 100 million (10^6) points

Potree, LAStools, Esri ArcGIS, PosgreSQL/PostGIS, PCL, FME, Own software

baumann@rasdaman.com

Photogrammetry, Subsurface Point Cloud from Seismic

Visualization, Digital Terrain Modelling

Temporal granularity at point level, Monitoring applications, change detection

Data Acquisition, Change Reference System

Data Model, DBMS / SQL, Web Service (WxxS) protocol

More than 100 million (10^6) points

LAS (ASPRS), LAZ, PCD, ASCII, PLY, Oracle SDO_PC, PostgreSQL-PostGIS PCPATCH, PDAL-generated blocks

LAZ, PDAL-generated blocks

Visualization, Digital Terrain Modelling

Hydrology, Civil Engineering

In a database, In the cloud

Timestamp, Intensity, Colour, Classification, Pulse Form, Pulse Count, Direction and Length of Scanline

To regular grid (raster), Level of Detail (LoD) representation

Temporal granularity at data set (a ‘point cloud’) level, Temporal resolution / update frequency years

Storage / Management, Dissemination, Visualization / Interaction

File Format / Encoding, Web Service (WxxS) protocol

More than 1 trillion (10^12) points

PDAL, Potree, LAStools, Oracle SDO_PC, PosgreSQL/PostGIS, PCL

Visualization, Digital Terrain Modelling, Feature Extraction

In a file on a computer, In a file on a network drive

Timestamp, Intensity, Colour, Classification, Pulse Form, Pulse Count, Direction and Length of Scanline, We keep everything we receive

To regular grid (raster), To TIN, Level of Detail (LoD) representation, To features (vector object rafter detection/recognition)

Temporal granularity at point level, Temporal granularity at data set (a ‘point cloud’) level

Combining Data from multiple source, Change Reference System, Analysis / Simluation, Dissemination, Visualization / Interaction

Data Model, File Format / Encoding, Web Service (WxxS) protocol

More than 1 trillion (10^12) points

PDAL, LAStools, GRASS, Esri ArcGIS, TerraSolid, LP360, VG4D, ENVI/IDL, C++

Our role at the USGS is a bit different from other users, as we are tasked with handling, managing, and exploiting data from ALL vendors, systems and other end users. And doing so in a project-agnostic, somewhat seamless manner.

Visualization, Digital Terrain Modelling, GIS

Hydrology, Water Management, Civil Engineering

In a file on a network drive

Timestamp, Intensity, Classification, Pulse Count

To regular grid (raster), To TIN

Temporal granularity at point level, Temporal granularity at data set (a ‘point cloud’) level, Temporal resolution / update frequency years, Temporal resolution / update frequency months, Monitoring applications, change detection

Combining Data from multiple source

Web Service (WxxS) protocol

More than 1 trillion (10^12) points

LAStools, homegrown tools for datum transformation.

We use a combination of things to manage data for distribution. While the data are left as LAZ files on network disks, the information about the data is stored in a database and we use that to find the data needed for a given request.

Don't use. Just interested.

Don't use. Just interested in what others do.

Don't use. Just interested in what others do.

Don't use. Just interested in what others do.

Don't use. Just interested in what others do.

Don't use. Just interested in what others do.

Don't use. Just interested in what others do.

Don't use. Just interested in what others do.

Don't use. Just interested in what others do.

Don't use. Just interested in what others do.

File Format / Encoding, Web Service (WxxS) protocol

Less than 100 million (10^6) points

Don't use. Just interested in what others do.

I am interested in the results of the survey WRT standards requirements.

Subsurface Point Cloud from Seismic

Visualization, Digital Terrain Modelling

In a file on a computer, In a file on a network drive

Monitoring applications, change detection

Storage / Management, Visualization / Interaction

File Format / Encoding, Web Service (WxxS) protocol

Less than 100 million (10^6) points

PosgreSQL/PostGIS, GMT, Meshlab, InstantReality, X3DOM

I've eavesdropped on the issue where vendor lock-in is being attempted for this important type of data. Our community is big enough to have a real community standard that avoids vendor lock-in.

Airborne LiDAR, Terrestrial Lidar (including Mobile Mapping), Photogrammetry, SONAR (single and multi-beam echo’s)

Visualization, Digital Terrain Modelling, Feature Extraction, GIS

Forestry, Hydrology, Water Management, Asset Management

In a file on a computer, In a file on a network drive, In a database, In the cloud

Timestamp, Intensity, Colour, Classification, Pulse Form, Pulse Count, Direction and Length of Scanline

To regular grid (raster), To TIN, Level of Detail (LoD) representation, To features (vector object rafter detection/recognition), None, direct use of point clouds

Temporal granularity at point level, Temporal granularity at data set (a ‘point cloud’) level, Temporal resolution / update frequency years, Temporal resolution / update frequency months, Monitoring applications, change detection

Storage / Management, Combining Data from multiple source, Change Reference System, Analysis / Simluation, Dissemination, Visualization / Interaction

More than 1 trillion (10^12) points

PDAL, Potree, LAStools, GRASS, Esri ArcGIS, TerraSolid

michael.rosen@gmail.com

Airborne LiDAR, Photogrammetry

Visualization, Digital Terrain Modelling, GIS

Intensity, Colour, Classification

To regular grid (raster), To TIN

Analysis / Simluation, Visualization / Interaction

Web Service (WxxS) protocol

More than 1 billion (10^9) points

LAStools, GRASS, Esri ArcGIS

Monterey Bay Aquarium Research Institute

Photogrammetry, SONAR (single and multi-beam echo’s), subsea lidar

MB-System supported sonar formats

MB-System supported sonar formats

Visualization, Digital Terrain Modelling, Feature Extraction, GIS

Marine geology and geophysics, seafloor habitat mapping

In a file on a computer, In a file on a network drive

Timestamp, Intensity, Colour, Pulse Form, Pulse Count, Direction and Length of Scanline, sonar attributes

To regular grid (raster), To TIN

Temporal granularity at point level, Monitoring applications, change detection

Data Acquisition, Storage / Management, Combining Data from multiple source, Change Reference System, Analysis / Simluation, Dissemination, Visualization / Interaction

Data Model, File Format / Encoding

More than 1 billion (10^9) points

My responses are from a seafloor mapping context. In the past most topography data derived from multibeam sonars, for which soundings have overlapping footprints rather than representing points, and were used to represent topography in 2D grids. We are now working with subsea lidar based mapping, plus mapping fully 3D features. Therefore the methods developed for working with point cloud data from subaerial lidars are increasingly relevant to seafloor mapping.

Terrestrial Lidar (including Mobile Mapping), Indoor Laser Scanning

E57, ASCII, PLY, PTX and various vendor specific formats

LAS (ASPRS), E57, ASCII, ptx

Digital Terrain Modelling, GIS, Fully 3D object modeling

Civil Engineering, VFX, terrain analysis

In a file on a computer, In a file on a network drive

Intensity, Colour, Classification, estimated surface normals, various arbitrary attributes

To regular grid (raster), None, direct use of point clouds, Fully 3D mesh, i.e. not a 2.5D TIN

Temporal granularity at data set (a ‘point cloud’) level, Monitoring applications, change detection

Storage / Management, Combining Data from multiple source, Analysis / Simluation, Dissemination

More than 1 billion (10^9) points

PDAL, Potree, GRASS, Esri ArcGIS, PCL, FARO Scene, PolyWorks, Sequoia

University of New Hampshire

Airborne LiDAR, Terrestrial Lidar (including Mobile Mapping), Photogrammetry, SONAR (single and multi-beam echo’s)

ASCII, CSAR, Any of various vendor formats, MATLAB .mat

Visualization, Feature Extraction

In a file on a computer, In a file on a network drive

Timestamp, Intensity, Measurement angle, vehicle location/attitude,

To regular grid (raster), To features (vector object rafter detection/recognition)

Temporal granularity at data set (a ‘point cloud’) level

Data Acquisition, Storage / Management, Combining Data from multiple source

More than 1 billion (10^9) points

CARIS, Fledermaus, MATLAB

UNAVCO / OpenTopography

Airborne LiDAR, Terrestrial Lidar (including Mobile Mapping), Photogrammetry

Visualization, Digital Terrain Modelling, Feature Extraction, GIS

Forestry, Hydrology, Geology, Geophysics, Geodesy, natural hazards, mapping, cryosphere and climate change, education/teaching

In a file on a computer, In a database, In the cloud, as files on high performance computers (HPC)

Timestamp, Intensity, Colour, Classification, full suite of ASPRS LAS attributes, scan position

To regular grid (raster), To TIN, Level of Detail (LoD) representation, To features (vector object rafter detection/recognition), None, direct use of point clouds, to voxel

Temporal granularity at point level, Temporal granularity at data set (a ‘point cloud’) level, Temporal resolution / update frequency years, Temporal resolution / update frequency months, Temporal resolution / update frequency days, Temporal resolution / update frequency seconds, Monitoring applications, change detection

Data Acquisition, Storage / Management, Combining Data from multiple source, Change Reference System, Analysis / Simluation, Dissemination, Visualization / Interaction

Data Model, File Format / Encoding, Web Service (WxxS) protocol

More than 1 trillion (10^12) points

PDAL, LAStools, Esri ArcGIS, PosgreSQL/PostGIS, PCL, RiScan Pro, libLAS, GDAL, Global Mapper, various academic/open source tools

derek.j.shockley@nga.mil

National Geospatial-Intelligence Agency

Airborne LiDAR, Terrestrial Lidar (including Mobile Mapping), Photogrammetry, RADAR (PS-InSAR)

Visualization, Digital Terrain Modelling, Feature Extraction, GIS

Timestamp, Intensity, Colour, Classification, Pulse Form, Pulse Count, Direction and Length of Scanline

None, direct use of point clouds

Temporal granularity at data set (a ‘point cloud’) level, Monitoring applications, change detection

Storage / Management, Combining Data from multiple source, Change Reference System, Visualization / Interaction

More than 1 billion (10^9) points

PDAL, PosgreSQL/PostGIS

Applied Research Assoc., Inc

Airborne LiDAR, Photogrammetry

Digital Terrain Modelling, Feature Extraction

Intelligence Community (IC) operations

In a file on a computer, In a file on a network drive

Intensity, Colour, Classification

To regular grid (raster), To features (vector object rafter detection/recognition)

Combining Data from multiple source, Change Reference System

Less than 100 million (10^6) points

LAStools, GRASS, PCL, FugroViewer, QT Reader

Airborne LiDAR, Terrestrial Lidar (including Mobile Mapping), Photogrammetry

In a file on a computer, servers

Intensity, Colour, Classification, normals

Level of Detail (LoD) representation

Combining Data from multiple source, Visualization / Interaction

File Format / Encoding, Web Service (WxxS) protocol

More than 1 billion (10^9) points

Potree, LAStools, CloudCompare

las/laz is nice and simple but I'd realy appreciated a format where you can selectively choose the attributes.
* Don't store more attributes than necessary
* Additionally to a set of standard attributs (position, classification, ...), let users define custom attributes.

Digital Terrain Modelling, Feature Extraction

In a file on a network drive, In the cloud

Timestamp, Intensity, Classification, Pulse Form, Pulse Count

To regular grid (raster), None, direct use of point clouds

Temporal granularity at data set (a ‘point cloud’) level, Temporal resolution / update frequency years, Monitoring applications, change detection

Storage / Management, Analysis / Simluation, Dissemination

File Format / Encoding, Indexing

More than 1 trillion (10^12) points

Airborne LiDAR, Terrestrial Lidar (including Mobile Mapping), Photogrammetry, SONAR (single and multi-beam echo’s), RADAR (PS-InSAR)

Visualization, Digital Terrain Modelling, Feature Extraction, GIS

Hydrology, Water Management, Smart City, Civil Engineering

In a file on a computer, In a file on a network drive

Timestamp, Intensity, Classification, Pulse Count, Direction and Length of Scanline

To regular grid (raster), To TIN, To features (vector object rafter detection/recognition)

Temporal granularity at point level, Temporal resolution / update frequency years, Temporal resolution / update frequency months, Temporal resolution / update frequency days, Temporal resolution / update frequency seconds, Monitoring applications, change detection

Combining Data from multiple source, Change Reference System, Dissemination

Data Model, File Format / Encoding, Web Service (WxxS) protocol

More than 1 billion (10^9) points

PDAL, LAStools, GRASS, Blue Marble and MARS7

mike.sutherland@noaa.gov

Airborne LiDAR, SONAR (single and multi-beam echo’s)

In a file on a computer, In a file on a network drive

Timestamp, Intensity, Colour, Classification, Pulse Count, Direction and Length of Scanline

To regular grid (raster), To TIN

Temporal granularity at point level, Temporal granularity at data set (a ‘point cloud’) level, Monitoring applications, change detection

Storage / Management, Combining Data from multiple source, Analysis / Simluation

More than 1 trillion (10^12) points

LAStools, Esri ArcGIS, CARIS, Fledermaus

Airborne LiDAR, Terrestrial Lidar (including Mobile Mapping), Indoor Laser Scanning, Photogrammetry

LAS (ASPRS), LAZ, ZLAS, ASCII, PLY

Visualization, Digital Terrain Modelling, Feature Extraction, GIS

Forestry, Hydrology, Smart City, Civil Engineering, Asset Management

In a file on a computer, In a file on a network drive, In a database, In the cloud

Timestamp, Intensity, Colour, Classification, Pulse Form, Pulse Count, Direction and Length of Scanline

To regular grid (raster), To TIN, Level of Detail (LoD) representation, To features (vector object rafter detection/recognition)

Temporal granularity at data set (a ‘point cloud’) level, Temporal resolution / update frequency years, Temporal resolution / update frequency seconds, Monitoring applications, change detection

Data Acquisition, Storage / Management, Combining Data from multiple source, Change Reference System, Analysis / Simluation, Dissemination, Visualization / Interaction

More than 1 trillion (10^12) points

PDAL, LAStools, Esri ArcGIS, PosgreSQL/PostGIS

Visualization, Feature Extraction, Coordination, Construction Quality Control

Asset Management, Construction

In a file on a computer, In a file on a network drive, In the cloud

None, direct use of point clouds

Temporal granularity at point level, Monitoring applications, change detection

Combining Data from multiple source, Analysis / Simluation, Visualization / Interaction

File Format / Encoding, Web Service (WxxS) protocol

More than 1 billion (10^9) points

PCL, Faro Scene, AutoDesk ReCap

vasanthmarvel@gmail.com

Airborne LiDAR, Terrestrial Lidar (including Mobile Mapping), Indoor Laser Scanning

LAS (ASPRS), LAZ, E57, POD, PLY, PostgreSQL-PostGIS PCPATCH, PDAL-generated blocks

LAS (ASPRS), E57, PLY, PostgreSQL-PostGIS PCPATCH

Visualization, Digital Terrain Modelling, Feature Extraction, GIS, BIM

Hydrology, Civil Engineering, Asset Management, BIM

In a file on a computer, In a file on a network drive, In a database

Timestamp, Intensity, Colour, Classification, Direction and Length of Scanline

To regular grid (raster), To TIN, Level of Detail (LoD) representation

Temporal resolution / update frequency years

Combining Data from multiple source, Dissemination

More than 1 billion (10^9) points

PDAL, Potree, LAStools, Esri ArcGIS, Bentley Pointools, PosgreSQL/PostGIS, TerraSolid, PCL, Revit

* There is an unnecessary brawl between Corporates and Openness regarding LAS1.4
* Point cloud streaming (Stored in PostGIS as pcpatch)/ Integration through Geoserver as standard option for Cesiumjs
* New LAS standard to reflect doppler capabilities of FMCW ladar

Institut Cartografic i Geologic de Catalunya

Airborne LiDAR, Photogrammetry

Digital Terrain Modelling, Feature Extraction

Forestry, Hydrology, Smart City

In a file on a computer, In a file on a network drive

Timestamp, Intensity, Classification

To regular grid (raster), To TIN, To features (vector object rafter detection/recognition)

Temporal resolution / update frequency years, Monitoring applications, change detection

Storage / Management, Analysis / Simluation

More than 1 billion (10^9) points

LAStools, GRASS, Esri ArcGIS, Bentley Pointools, Leica CloudWorx, PosgreSQL/PostGIS, TerraSolid

o.rubi@esciencecenter.nl

Netherlands eScience Center

Airborne LiDAR, Terrestrial Lidar (including Mobile Mapping), Photogrammetry

LAS (ASPRS), LAZ, Oracle SDO_PC, PostgreSQL-PostGIS PCPATCH, PDAL-generated blocks, Flat table

In a file on a computer, In a database, In the cloud

Level of Detail (LoD) representation, None, direct use of point clouds

Temporal granularity at data set (a ‘point cloud’) level, Temporal resolution / update frequency years

Storage / Management, Combining Data from multiple source, Visualization / Interaction

Web Service (WxxS) protocol

More than 1 billion (10^9) points

PDAL, Potree, LAStools, Oracle SDO_PC, PosgreSQL/PostGIS, MonetDB

douglas.daniel@subsea7.com

Indoor Laser Scanning, Photogrammetry, SONAR (single and multi-beam echo’s)

Visualization, Digital Terrain Modelling

Asset Management, Oil and Gas

In a file on a network drive

To regular grid (raster), None, direct use of point clouds

Temporal granularity at point level, Temporal granularity at data set (a ‘point cloud’) level

More than 100 million (10^6) points

Potree, LAStools, Esri ArcGIS, EIVA NaviModel

charliethomson@gmail.com

Terrestrial Lidar (including Mobile Mapping), Indoor Laser Scanning

Visualization, Feature Extraction

Civil Engineering, Asset Management

In a file on a computer, In a file on a network drive

To features (vector object rafter detection/recognition), None, direct use of point clouds

Temporal granularity at point level, Temporal resolution / update frequency seconds, Monitoring applications, change detection

Analysis / Simluation, Dissemination, Visualization / Interaction

More than 1 billion (10^9) points

LAS (ASPRS), LAZ, Flat table

Visualization, Digital Terrain Modelling, Feature Extraction, GIS

Forestry, Hydrology, Water Management

In a file on a computer, In a file on a network drive

Timestamp, Intensity, Classification

To regular grid (raster), To TIN, To features (vector object rafter detection/recognition), None, direct use of point clouds

Monitoring applications, change detection

Data Acquisition, Storage / Management, Combining Data from multiple source, Change Reference System

More than 100 million (10^6) points

LAStools, Esri ArcGIS, LP360

richard@polarisgeomatics.com

Visualization, Feature Extraction

Civil Engineering, Asset Management

In a file on a network drive

Timestamp, Intensity, Colour, Classification

Level of Detail (LoD) representation, To features (vector object rafter detection/recognition)

Temporal granularity at point level, Temporal resolution / update frequency years

Data Acquisition, Combining Data from multiple source, Visualization / Interaction

Data Model, File Format / Encoding

More than 1 trillion (10^12) points

benjamin.h.houston@gmail.com

Airborne LiDAR, UAV based Photogrammetry

LAS (ASPRS), LAZ, ESRI LASD

Digital Terrain Modelling, Feature Extraction, GIS

Hydrology, Civil Engineering, Asset Management, Site Characterization

In a file on a computer, In the cloud, Cloud = Dropbox

Intensity, Colour, Classification, Pulse Count

To regular grid (raster), To TIN, Extract vectors, not direct conversion

Temporal granularity at data set (a ‘point cloud’) level, Temporal resolution / update frequency years, Temporal resolution / update frequency months, Temporal resolution / update frequency days, Monitoring applications, change detection, UAV vs airborne have different temporal resolution relevancies

Analysis / Simluation, Dissemination

Web Service (WxxS) protocol

More than 100 million (10^6) points

LAStools, Esri ArcGIS, LP360, Pix4D

peter.bonne@orbitgt.com

Terrestrial Lidar (including Mobile Mapping), Indoor Laser Scanning

LAS (ASPRS), LAZ, E57, ASCII, PLY, Flat table

Visualization, Feature Extraction, Profiling, Clash detection, Volumetric Analysis, ...

Asset Management, Road Admin, Rail, Tunneling, Utilities, ...

In a file on a computer, In a file on a network drive, In the cloud

Timestamp, Intensity, Colour, Classification, Pulse Form, Pulse Count, Direction and Length of Scanline, Scanner #

To features (vector object rafter detection/recognition), None, direct use of point clouds, Textured Meshes, Orthophoto, ...

Temporal granularity at data set (a ‘point cloud’) level, Monitoring applications, change detection

Our software solves these problems ;-)

Data Model, File Format / Encoding, Web Service (WxxS) protocol

More than 1 trillion (10^12) points

Terrestrial Lidar (including Mobile Mapping), Indoor Laser Scanning, Photogrammetry

LAS (ASPRS), LAZ, E57, PCD, POD, ASCII, PLY, Pts, ptx

LAS (ASPRS), LAZ, E57, POD, PLY

Visualization, Digital Terrain Modelling, Feature Extraction

Civil Engineering, Industrial, plants

In a file on a network drive

None, direct use of point clouds

Temporal granularity at point level, Monitoring applications, change detection

Combining Data from multiple source

More than 1 billion (10^9) points

Potree, LAStools, Bentley Pointools, Leica CloudWorx, FME

Airborne LiDAR, Terrestrial Lidar (including Mobile Mapping), Photogrammetry, SONAR (single and multi-beam echo’s)

Visualization, Digital Terrain Modelling, Feature Extraction, GIS, geological/sedimentary description/quantification

sedimentology, geomorphology

In a file on a computer, In a file on a network drive

Timestamp, Intensity, acoustic backscatter, acoustic variables such as pulse length, beam direction, etc. VVV imp for sonar processing that all these things are kept together

To regular grid (raster), None, direct use of point clouds

Temporal resolution / update frequency years, Temporal resolution / update frequency months, Temporal resolution / update frequency days, Temporal resolution / update frequency seconds, Monitoring applications, change detection

Dissemination, Visualization / Interaction

Data Model, File Format / Encoding

More than 1 billion (10^9) points

Hypack. Plus I developed my own tools: http://dbuscombe-usgs.github.io/pysesa/

There is a pressing need for standardizing point cloud formats from terrestrial (lidar / laser scanning / sfm, etc) and from sonar systems (multibeam, sidescan, singlebeam). Increasingly, the two are being used together in single survey systems, simultaneously acquiring terrestrial and bathymetric data. Current data formats cannot contain all required attributes from lidar and sonar. Sonar analysis needs fields for backscatter, launch angles, beam numbers, and time-varying acoustic variables such as pulse length, frequency, etc.

University of Copenhagen

Feature Extraction, calculation of descriptive statistics of return heights for modeling vegetation characteristics

In a file on a computer, In a file on a network drive, external HDD

Intensity, Classification, Pulse Form, Pulse Count

None, direct use of point clouds

Monitoring applications, change detection

Change Reference System

More than 100 million (10^6) points

LAStools, Esri ArcGIS, Fusion LDV

Airborne LiDAR, Terrestrial Lidar (including Mobile Mapping)

Forest biomass assessments

In a file on a computer, In the cloud

Timestamp, Intensity, Classification, Pulse Form, Pulse Count, Direction and Length of Scanline

None, direct use of point clouds

Temporal granularity at point level, Temporal resolution / update frequency days, Monitoring applications, change detection

Storage / Management, Combining Data from multiple source

File Format / Encoding, DBMS / SQL

More than 100 million (10^6) points

Airborne LiDAR, Terrestrial Lidar (including Mobile Mapping), Photogrammetry

Visualization, Digital Terrain Modelling, Feature Extraction, GIS, Volumetric Analysis

Hydrology, Smart City, Civil Engineering, Mining

In a file on a computer, In a file on a network drive, In the cloud, LIDARServer (www.lidarserver.com)

Timestamp, Intensity, Colour, Classification, Pulse Count, Direction and Length of Scanline, Normal Vector

To regular grid (raster), To TIN, To features (vector object rafter detection/recognition)

Temporal resolution / update frequency years, Temporal resolution / update frequency months, Temporal resolution / update frequency days, Monitoring applications, change detection, Granulatiry at the block and polygon level

Never. Everyone seems to uderstand LAS

File Format / Encoding, Web Service (WxxS) protocol

More than 1 trillion (10^12) points

Esri ArcGIS, TerraSolid, LP360, LIDARServer

LP360 is a standard tool used by USGS, USDA, many state, local governments. Should probably be on your survey.

Indoor Laser Scanning, Photogrammetry

Feature Extraction, Measurement

Asset Management, as-built documentation

In a file on a computer, In a file on a network drive, In the cloud

Timestamp, Intensity, Colour, Images

To features (vector object rafter detection/recognition), point to point measurement

Temporal granularity at data set (a ‘point cloud’) level

Storage / Management, Combining Data from multiple source, Dissemination, Visualization / Interaction

File Format / Encoding, Auxillary data

More than 100 million (10^6) points

LAStools, Faro Scene, Leica Cyclone

I am working with the E57 committee to extend the current standard for use as an archive of point cloud and image data generated during construction.

seanwilliammorrish@gmail.com

Airborne LiDAR, Terrestrial Lidar (including Mobile Mapping), Indoor Laser Scanning, Photogrammetry

LAS (ASPRS), LAZ, ZLAS, E57, ASCII, PDAL-generated blocks

Visualization, Digital Terrain Modelling, Feature Extraction, GIS

Hydrology, Smart City, Civil Engineering, Asset Management

In a file on a network drive, In a database, In the cloud

Timestamp, Intensity, Colour, Classification, Direction and Length of Scanline

To regular grid (raster), To TIN, Level of Detail (LoD) representation, To features (vector object rafter detection/recognition)

Temporal resolution / update frequency years, Monitoring applications, change detection

Data Acquisition, Storage / Management, Combining Data from multiple source, Change Reference System, Analysis / Simluation, Dissemination

Data Model, File Format / Encoding, Web Service (WxxS) protocol

More than 1 billion (10^9) points

PDAL, Potree, LAStools, Esri ArcGIS, Oracle SDO_PC, FME

fgoncalves@mcelhanney.com

Airborne LiDAR, Terrestrial Lidar (including Mobile Mapping), Photogrammetry

Digital Terrain Modelling, Feature Extraction, GIS, Slope analysis, Vol Calculations, watershed, Feature Detection

Forestry, Hydrology, Water Management, Civil Engineering, Asset Management, Archaeology, Agribusiness, Environment

In a file on a network drive

Intensity, Colour, Classification, Feature extraction, profiling, X-Sections, Volume Calculations, Time Change

Level of Detail (LoD) representation

Temporal granularity at point level, Monitoring applications, change detection

Combining Data from multiple source, Dissemination

Data Model, File Format / Encoding

More than 1 trillion (10^12) points

Esri ArcGIS, Bentley Pointools, TerraSolid

mike.meiser@woolpert.com

Airborne LiDAR, Terrestrial Lidar (including Mobile Mapping)

Digital Terrain Modelling, Feature Extraction, GIS

Forestry, Hydrology, Water Management, Smart City, Civil Engineering, Asset Management

In a file on a computer, In a file on a network drive, In a database

Timestamp, Intensity, Colour, Classification, Pulse Form, Pulse Count, Direction and Length of Scanline

To regular grid (raster), To TIN

Temporal resolution / update frequency years, Monitoring applications, change detection

Combining Data from multiple source

More than 1 trillion (10^12) points

LAStools, Esri ArcGIS, TerraSolid, TopoDOT, FME, QT modeler, global mapper, LP360, Geocue

Airborne LiDAR, Terrestrial Lidar (including Mobile Mapping), Photogrammetry

Visualization, Digital Terrain Modelling, Feature Extraction, GIS

Forestry, Hydrology, Water Management, Civil Engineering, Asset Management

In a file on a computer, In a file on a network drive

Timestamp, Intensity, Colour, Classification, pulse source ID (flightline ID), scan angle, withheld flag, overlap flag, number of returns, return count, user data

To regular grid (raster), To TIN, To features (vector object rafter detection/recognition), None, direct use of point clouds

as long as we use LAS/LAZ there are no interoperability challenges

widely existing support in all major software packages

More than 1 billion (10^9) points

Potree, LAStools, GRASS, TopoDOT, QGIS, FUSION, FugroViewer, QT Reader, CloudCompare, RiProcess, CloudPro, Optech LMS 3.0, Trimble RealWorks, Envi LiDAR, Leica LIDAR Survey Studio LSS, ZEB1, Voyager, Scop++, DTMaster

The list of LiDAR software is rather incomplete.

Airborne LiDAR, Photogrammetry

Visualization, Digital Terrain Modelling

In a file on a network drive

Timestamp, Intensity, Classification, Pulse Count

Level of Detail (LoD) representation, None, direct use of point clouds

Temporal granularity at data set (a ‘point cloud’) level

Dissemination, Visualization / Interaction

Data Model, Web Service (WxxS) protocol

More than 1 billion (10^9) points

Bentley Pointools, TerraSolid

National Oceanography Centre

SONAR (single and multi-beam echo’s)

To regular grid (raster), To TIN, To features (vector object rafter detection/recognition)

Temporal resolution / update frequency years, Temporal resolution / update frequency months

More than 100 million (10^6) points

Esri ArcGIS, CloudCompare

Airborne LiDAR, RADAR (PS-InSAR)

Visualization, Digital Terrain Modelling, GIS

Hydrology, Asset Management

In a file on a network drive, In the cloud

Timestamp, Intensity, Colour, Classification, Direction and Length of Scanline

To regular grid (raster), To TIN

Temporal resolution / update frequency years, Monitoring applications, change detection

Web Service (WxxS) protocol

More than 1 trillion (10^12) points

LAStools, homemade scripts/tools

Airborne LiDAR, Photogrammetry

Visualization, Digital Terrain Modelling, Feature Extraction, GIS

Forestry, Civil Engineering, Asset Management

In a file on a network drive, In a database, In the cloud

Timestamp, Intensity, Colour, Classification, Pulse Form, Pulse Count, Direction and Length of Scanline

To regular grid (raster), To TIN, To features (vector object rafter detection/recognition)

Temporal granularity at point level, Temporal granularity at data set (a ‘point cloud’) level, Temporal resolution / update frequency years, Temporal resolution / update frequency months, Temporal resolution / update frequency days, Temporal resolution / update frequency seconds, Monitoring applications, change detection

Data Acquisition, Combining Data from multiple source

Data Model, File Format / Encoding, DBMS / SQL

More than 100 million (10^6) points

LAStools, Esri ArcGIS, TerraSolid, Inpho

SONAR (single and multi-beam echo’s)

Visualization, Digital Terrain Modelling

In a file on a network drive

Temporal resolution / update frequency years

Storage / Management, Combining Data from multiple source

More than 1 billion (10^9) points

Airborne LiDAR, Terrestrial Lidar (including Mobile Mapping), Indoor Laser Scanning, Photogrammetry

LAS (ASPRS), LAZ, ZLAS, POD

Digital Terrain Modelling, Feature Extraction

Civil Engineering, Asset Management

Timestamp, Intensity, Colour, Classification

To regular grid (raster), To TIN, To features (vector object rafter detection/recognition)

Temporal granularity at data set (a ‘point cloud’) level, Temporal resolution / update frequency months, Monitoring applications, change detection

Storage / Management, Combining Data from multiple source

More than 1 billion (10^9) points

Potree, LAStools, GRASS, Esri ArcGIS, Bentley Pointools, Leica CloudWorx

sanjosealbacete@yahoo.es

Airborne LiDAR, RADAR (PS-InSAR)

Visualization, Digital Terrain Modelling, Feature Extraction, GIS

Forestry, Hydrology, Archaeology

In a file on a computer, In a database

Intensity, Classification, Direction and Length of Scanline

To features (vector object rafter detection/recognition), None, direct use of point clouds

Monitoring applications, change detection

Storage / Management, Combining Data from multiple source

Data Model, File Format / Encoding

Less than 100 million (10^6) points

LAStools, GRASS, Esri ArcGIS, PosgreSQL/PostGIS, TerraSolid, FME

Airborne LiDAR, Terrestrial Lidar (including Mobile Mapping), RADAR (PS-InSAR)

LAS (ASPRS), LAZ, PulseWaves

LAS (ASPRS), LAZ, PulseWaves

Visualization, Digital Terrain Modelling, Feature Extraction, tree extraction

In a file on a computer, In a file on a network drive

Intensity, Pulse Form, full-waveform analysis

None, direct use of point clouds

Temporal resolution / update frequency years, Monitoring applications, change detection

File Format / Encoding, industry needs to move towards Full-Waveform rather than just point cloud

More than 1 trillion (10^12) points

Potree, LAStools, GRASS, PCL, this list misses CloudCompare - we also use our in-house tools for full-waveform processing

Airborne LiDAR, Terrestrial Lidar (including Mobile Mapping), Indoor Laser Scanning, Photogrammetry

LAS (ASPRS), LAZ, POD, ASCII

Visualization, Digital Terrain Modelling, Feature Extraction

Smart City, Heritage Documentation

In a file on a computer, In a file on a network drive

Intensity, Colour, Classification

To regular grid (raster), To TIN, Level of Detail (LoD) representation, To features (vector object rafter detection/recognition)

Temporal granularity at data set (a ‘point cloud’) level, Monitoring applications, change detection

Combining Data from multiple source, Analysis / Simluation

Data Model, Web Service (WxxS) protocol

More than 1 trillion (10^12) points

LAStools, Bentley Pointools, Leica CloudWorx, SOCET SET / DTM Master

Visualization, Digital Terrain Modelling, Feature Extraction, GIS

In a file on a computer, In a file on a network drive

Timestamp, Colour, Classification

To regular grid (raster), To TIN, To features (vector object rafter detection/recognition), 3D mesh

Temporal granularity at point level

Storage / Management, Analysis / Simluation

More than 1 billion (10^9) points

Esri ArcGIS, TerraSolid, VR Mesh

james.holmes@renishaw.com

Terrestrial Lidar (including Mobile Mapping), SONAR (single and multi-beam echo’s)

LAS (ASPRS), LAZ, POD, ASCII, La20 (Trimble format)

Visualization, Digital Terrain Modelling, Feature Extraction

Hydrology, Civil Engineering, Asset Management

In a file on a computer, In a file on a network drive

Timestamp, Intensity, Classification

To TIN, None, direct use of point clouds

Monitoring applications, change detection

Combining Data from multiple source, Change Reference System, Analysis / Simluation

More than 1 billion (10^9) points

Bentley Pointools, TerraSolid, Hydrographic software (e.g. QINSy, HYPACK, PDS), Trimble's Trident software, Maptek's I-Site Studio

karol.krajewski@nmgroup.com

Digital Terrain Modelling, Feature Extraction, GIS

Asset Management, Utilities

Timestamp, Intensity, Colour, Classification, Pulse Count, Direction and Length of Scanline

To regular grid (raster), To TIN, None, direct use of point clouds

Temporal resolution / update frequency years, Monitoring applications, change detection

Combining Data from multiple source, Visualization / Interaction

DBMS / SQL, Web Service (WxxS) protocol

More than 1 trillion (10^12) points

Potree, LAStools, Bentley Pointools, TerraSolid, FME

Ian@indeep-consulting.uk

Airborne LiDAR, SONAR (single and multi-beam echo’s)

Civil Engineering, Coastal zone management

In a file on a network drive

Timestamp, Intensity, Classification, Pulse Form, Pulse Count, Direction and Length of Scanline

To regular grid (raster), To TIN, Level of Detail (LoD) representation, To features (vector object rafter detection/recognition)

Temporal resolution / update frequency years

Storage / Management, Combining Data from multiple source, Visualization / Interaction

More than 1 billion (10^9) points

LAStools, GRASS, CARIS, Fledermaus, TerraSolid

lee.mcdougall@ahr-global.com

Terrestrial Lidar (including Mobile Mapping), Indoor Laser Scanning

E57, POD, PTS, PTX, PCG, RCS

Visualization, Digital Terrain Modelling, Feature Extraction, Building Modelling

In a file on a network drive, data centre, tape

None, direct use of point clouds

Temporal granularity at data set (a ‘point cloud’) level

More than 1 trillion (10^12) points

Leica CloudWorx, Recap, Point tools, Scene

Airborne LiDAR, Terrestrial Lidar (including Mobile Mapping), Indoor Laser Scanning, Photogrammetry

LAS (ASPRS), PLY, PostgreSQL-PostGIS PCPATCH

Visualization, Feature Extraction, City modelling, surface reconstruction, forestry

Forestry, Smart City, Civil Engineering, Asset Management

In a file on a computer, In a file on a network drive, In a database, In the cloud

Timestamp, Intensity, Colour, Classification, Pulse Form, Pulse Count, Direction and Length of Scanline, sensor position !!!

Level of Detail (LoD) representation, To features (vector object rafter detection/recognition), None, direct use of point clouds

Temporal granularity at point level, Temporal granularity at data set (a ‘point cloud’) level, Temporal resolution / update frequency years, Temporal resolution / update frequency months, Temporal resolution / update frequency days, Monitoring applications, change detection

Change Reference System, Analysis / Simluation, Dissemination

Data Model, Web Service (WxxS) protocol

More than 1 trillion (10^12) points

PDAL, Potree, LAStools, PosgreSQL/PostGIS, PCL

adam.d.steer@gmail.com

National Computational infrastructure (Australia), ACE-CRC

Airborne LiDAR, Photogrammetry

LAS (ASPRS), ASCII, netCDF

Visualization, as a start point for estimating characteristics of sea ice

marine cryosphere characterisation

In a file on a computer, In a file on a network drive

Timestamp, Intensity, scan angle, position uncertainty (x,y,z,3d), sometimes trajectory information

None, direct use of point clouds

Temporal granularity at point level, Temporal granularity at data set (a ‘point cloud’) level

Data Acquisition, Storage / Management

More than 100 million (10^6) points

GRASS, Bentley Pointools, TerraSolid, PCL, CloudCompare, Python, MATLAB, QGIS

My work with point clouds has been really research-focussed. I've used 'standard' tools for some of my work (eg. terrasolid), but mainly work with customised coordinate systems and therefore customised software from raw laser data /aircraft trajectories -> point clouds.

A big issue for me has been keeping all the data managed, since I have to track all the data and processing prior to spitting out a point cloud (from either lasers or cameras). So provenance, or lack thereof, is something I need to solve.

I haven't used .las much purely because it is harder to work with than just messing around with text files (also awkward). I see a future in HDF formats (eg HDF or it's subset netCDF), because on that path I can quickly grab data in subsets, I can also track provenance, and versions, and ....

In terrasolid, for example, the concept of flight lines is key to making things happen. In my mind it would be possible to index a netcdf file by line, and have some pretty simple wrappers to pull out data in whatever format desired (eg; pointcloud.tolas(data), or pointcloud.ingest(lasfile, line, ...)

For 3D photogrammetry, for example, the point cloud is not really 4D but the aircraft trajectory is. If one wants to keep provenance, storing these things gets tricky unless a HDF style scheme is used.

Anyway - it gets complicated. and I see only one small speck of the point cloud world. However - ina world moving toward semantic web style operations, I see no reason to commit all point clouds to a specific and rigid format, as much as some vendors would like.

In a way, if there exists a metadata specification that is used everywhere, and can refer to a long list of formats, and the tools exist to translate, then a good and accepted metadata standard is possibly the best approach I can see. After all, data are just bits on a disk - the trick is making those bits meaningful!

Cheers
Adam

Airborne LiDAR, Terrestrial Lidar (including Mobile Mapping), Photogrammetry

Visualization, Digital Terrain Modelling, geological interpretation

In a file on a computer, In a file on a network drive

Level of Detail (LoD) representation, octree

Monitoring applications, change detection

Data Model, File Format / Encoding, Web Service (WxxS) protocol

More than 1 billion (10^9) points

LAStools, Esri ArcGIS, FME, GeoVisionary

eberhard.guelch@hft-stuttgart.de

Airborne LiDAR, Terrestrial Lidar (including Mobile Mapping), Indoor Laser Scanning, Photogrammetry

Visualization, Digital Terrain Modelling, Feature Extraction

Timestamp, Intensity, Colour, Classification

To regular grid (raster), To features (vector object rafter detection/recognition)

Temporal granularity at point level

Combining Data from multiple source

Less than 100 million (10^6) points

Terrestrial Lidar (including Mobile Mapping)

Digital Terrain Modelling, Feature Extraction

Civil Engineering, Land Surveying Topo

In a file on a network drive

None, direct use of point clouds

Temporal resolution / update frequency years, Temporal resolution / update frequency months, Monitoring applications, change detection

Data Acquisition, Visualization / Interaction

Data Model, File Format / Encoding

More than 100 million (10^6) points

Airborne LiDAR, Terrestrial Lidar (including Mobile Mapping), Photogrammetry

LAS (ASPRS), LAZ, 3D shapefile

Visualization, Digital Terrain Modelling, GIS

Geology and general Terrain Analysis

Timestamp, Intensity, Colour, Classification, Direction and Length of Scanline

To regular grid (raster), None, direct use of point clouds

Monitoring applications, change detection

Combining Data from multiple source

More than 1 billion (10^9) points

LAStools, MICRODEM, Cloud Compare

Any formats should be open with the specifications published. No format should lock users into a particular proprietary software package.

Software should be free to optimize data formats for internal use, but the distribution formats should be open and documented

tobias.kohoutek@digimapas.cl

Airborne LiDAR, Terrestrial Lidar (including Mobile Mapping), Photogrammetry

LAS (ASPRS), LAZ, ASCII, SPD

Digital Terrain Modelling, Feature Extraction

In a file on a network drive, In a database

Timestamp, Intensity, Classification, Pulse Count, Direction and Length of Scanline

To regular grid (raster), To TIN

Temporal granularity at point level, Monitoring applications, change detection

Storage / Management, Analysis / Simluation

Web Service (WxxS) protocol

More than 100 million (10^6) points

LAS (ASPRS), LAZ, PDAL-generated blocks

In a file on a computer, In a file on a network drive

Timestamp, Intensity, Classification

To regular grid (raster), To TIN

Temporal granularity at data set (a ‘point cloud’) level

Data Acquisition, Combining Data from multiple source

Data Model, File Format / Encoding

More than 100 million (10^6) points

PDAL, LAStools, PosgreSQL/PostGIS

Airborne LiDAR, Terrestrial Lidar (including Mobile Mapping)

LAS (ASPRS), ASCII, SQLite

LAS (ASPRS), ASCII, SQLite

Digital Terrain Modelling, Feature Extraction, GIS

Forestry, Hydrology, Water Management

Timestamp, Intensity, Colour, Classification, Pulse Form, Pulse Count

To regular grid (raster), None, direct use of point clouds

Temporal granularity at point level, Monitoring applications, change detection

Data Acquisition, Storage / Management, Combining Data from multiple source, Change Reference System, Analysis / Simluation, Dissemination, Visualization / Interaction

File Format / Encoding, DBMS / SQL

More than 100 million (10^6) points

LAStools, Custom written LASF access

I would strongly suggest investigating SQLite as a storage container. LASF is okay, but SQLite gives access to many programming and scripting languages with out extra work. APIs are easily written without 3rd party libraries, and can be written in native programming languages. Also, recent releases of SQLite have built in R*Tree based spatial indexing. GeoPackage uses SQLite for a good reason.

Airborne LiDAR, Terrestrial Lidar (including Mobile Mapping), Photogrammetry

Visualization, Digital Terrain Modelling, Feature Extraction

Timestamp, Intensity, Colour, Pulse Count

To regular grid (raster), To features (vector object rafter detection/recognition)

Monitoring applications, change detection

Data Acquisition, Combining Data from multiple source, Change Reference System, Analysis / Simluation, Visualization / Interaction

File Format / Encoding, ideally open format and widely used (las/laz)

More than 100 million (10^6) points

LAStools, Esri ArcGIS, FME, eCognition, ENVI

I've been following that whole laz vs zlas conundrum, and don't understand why ESRI had to come up with their own closed proprietary format. Seemed to me that laz was doing all that zlas is doing and Martin from lastools was cooperative into adding further functionality if needed. So why duplicate the effort and create more work converting back and forth unnecessarily???

I like the ArcMap Lidar functionality and use it quite a bit but it annoys me a lot that I cannot use laz files directly and so far I haven't seen any advantage (or actual use) for zlas.

I'm sure there's lots of politics involved there, but I'm hoping that whole zlas thing doesn't take off and companies that have embraced the laz approach stick with it and make it the de facto standard.

Forestry, Hydrology, Smart City

In a file on a network drive

Intensity, Colour, Classification

To regular grid (raster), To features (vector object rafter detection/recognition)

Temporal granularity at data set (a ‘point cloud’) level

More than 1 trillion (10^12) points

LAStools, Esri ArcGIS, FME, Quick Terrain Modeler

The biggest threat to LiDAR is proprietary formats. Keep LiDAR open!

Ramesh.Sridharan@Autodesk.com

Airborne LiDAR, Terrestrial Lidar (including Mobile Mapping), Indoor Laser Scanning, Photogrammetry

LAS (ASPRS), E57, ASCII, RCS

Visualization, Digital Terrain Modelling, Feature Extraction, GIS, 3D Modeling

Forestry, Hydrology, Water Management, Smart City, Civil Engineering, Asset Management

In a file on a computer, In a file on a network drive, In the cloud

Timestamp, Intensity, Colour, Classification

To regular grid (raster), To TIN, Level of Detail (LoD) representation, To features (vector object rafter detection/recognition), None, direct use of point clouds

Temporal granularity at point level, Temporal granularity at data set (a ‘point cloud’) level, Monitoring applications, change detection

Analysis / Simluation, Visualization / Interaction

Data Model, File Format / Encoding

More than 1 trillion (10^12) points

Autodesk products like ReCap and Infraworks

SDFE - the Danish National Mapping Authority

In a file on a network drive, In a database

Timestamp, Intensity, Colour, Classification, Pulse Count, Full LAS1.3 gamut

To regular grid (raster), To TIN, None, direct use of point clouds

Temporal granularity at point level, Monitoring applications, change detection, Granularity at Data subset (tile) level

None. LAS ensures interoperability

File Format / Encoding, Web Service (WxxS) protocol

More than 1 billion (10^9) points

PDAL, Potree, LAStools, PosgreSQL/PostGIS, TerraSolid, QGIS

I find Esri's pushing for a closed file format (zlas) absolutely detrimental to interoperability, and countering a National Mapping Authority mission of ensuring long term access and interoperability of elevation data.

Please have this in mind in OGC's further work om point cloud data

Airborne LiDAR, Terrestrial Lidar (including Mobile Mapping)

Visualization, Feature Extraction

Water Management, Civil Engineering, Asset Management

Timestamp, Intensity, Colour, Classification, Direction and Length of Scanline

To regular grid (raster), To TIN, To features (vector object rafter detection/recognition), None, direct use of point clouds

Temporal granularity at point level, Temporal granularity at data set (a ‘point cloud’) level

Data Acquisition, Storage / Management, Combining Data from multiple source, Analysis / Simluation, Visualization / Interaction

Data Model, File Format / Encoding

More than 1 billion (10^9) points

PDAL, LAStools, GRASS, Esri ArcGIS, Leica CloudWorx, TerraSolid, FME, Leica cyclone and map factory

doug_newcomb@fws.gov

Digital Terrain Modelling, Feature Extraction

Timestamp, Intensity, Colour, Classification, Pulse Count, Direction and Length of Scanline

Temporal granularity at point level, Temporal resolution / update frequency years, Monitoring applications, change detection

Data Acquisition, Combining Data from multiple source, Change Reference System

More than 1 billion (10^9) points

jacob.edwards@dogami.state.or.us

Oregon Dpeartment of Geology and Mineral Industries

Visualization, Digital Terrain Modelling, Feature Extraction, GIS

Forestry, Hydrology, Water Management, Civil Engineering

In a file on a network drive

Timestamp, Intensity, Colour, Classification, Pulse Form, Pulse Count, Direction and Length of Scanline

Temporal resolution / update frequency years

Storage / Management, Change Reference System, Dissemination

More than 1 trillion (10^12) points

LAStools, Esri ArcGIS, Bentley Pointools, TerraSolid, Quick Terrain Modeler

I prefer open LiDAR formats over proprietary clones

david.webb@transgrid.com.au

Airborne LiDAR, Terrestrial Lidar (including Mobile Mapping)

LAS (ASPRS), LAZ, POD, IMP

Visualization, Digital Terrain Modelling, Feature Extraction

Forestry, Civil Engineering, Asset Management

In a file on a network drive

Intensity, Colour, Classification

To regular grid (raster), To TIN, To features (vector object rafter detection/recognition), None, direct use of point clouds

Temporal resolution / update frequency years, Monitoring applications, change detection

Storage / Management, Combining Data from multiple source

More than 1 trillion (10^12) points

Potree, LAStools, Bentley Pointools, Leica CloudWorx, TerraSolid, FME, Leica Cyclone

kari.salovaara@pp1.inet.fi

LAZ, PostgreSQL-PostGIS PCPATCH, PDAL-generated blocks

LAZ, PostgreSQL-PostGIS PCPATCH, PDAL-generated blocks

Digital Terrain Modelling, GIS

In a file on a computer, In a database

Timestamp, Intensity, Colour, Classification, Pulse Form, Pulse Count, Direction and Length of Scanline

To regular grid (raster), To TIN, Level of Detail (LoD) representation, To features (vector object rafter detection/recognition)

Temporal granularity at data set (a ‘point cloud’) level

Data Acquisition, Combining Data from multiple source, Analysis / Simluation, Dissemination

Data Model, File Format / Encoding, DBMS / SQL

More than 100 million (10^6) points

PDAL, LAStools, GRASS, PosgreSQL/PostGIS

floris.groesz@blomasa.com

Airborne LiDAR, Photogrammetry

LAS (ASPRS), LAZ, Terrasolid Fast Binary

Visualization, Digital Terrain Modelling, Feature Extraction, Modelling of forest variables

In a file on a network drive

Timestamp, Intensity, Colour, Classification, Pulse Count, Direction and Length of Scanline

To regular grid (raster), To features (vector object rafter detection/recognition), None, direct use of point clouds

Temporal granularity at point level, Temporal granularity at data set (a ‘point cloud’) level, Monitoring applications, change detection

Data Model, File Format / Encoding

More than 1 billion (10^9) points

LAStools, Esri ArcGIS, TerraSolid, eCognition, Global Mapper, R

The name "zlas" by ESRI and the term "optimized LAS" is confusing. There should be a clear distinction between open/standard formats and proprietary formats, also for compressed versions.
The success of the LAS format has been of great benefit to all producers and users of point cloud data.
This success should not be nullified during shift to compressed formats.

Airborne LiDAR, Terrestrial Lidar (including Mobile Mapping), Indoor Laser Scanning

Visualization, Digital Terrain Modelling, Feature Extraction, GIS

Hydrology, Civil Engineering, Asset Management

In a file on a computer, In a file on a network drive, In a database, In the cloud

Intensity, Colour, Classification, Normals

To TIN, Level of Detail (LoD) representation

Temporal granularity at data set (a ‘point cloud’) level

Storage / Management, Combining Data from multiple source, Change Reference System, Dissemination, Visualization / Interaction

File Format / Encoding, Web Service (WxxS) protocol

More than 1 billion (10^9) points

LAStools, Leica CloudWorx

uwe.bacher@geo4you.de

Digital Terrain Modelling, GIS

Timestamp, Intensity, Colour

To regular grid (raster), None, direct use of point clouds

Monitoring applications, change detection

Analysis / Simluation, Visualization / Interaction

Data Model, File Format / Encoding

More than 100 million (10^6) points

hayakawa@csis.u-tokyo.ac.jp

Airborne LiDAR, Terrestrial Lidar (including Mobile Mapping), Indoor Laser Scanning, Photogrammetry

Digital Terrain Modelling, Feature Extraction, GIS

To regular grid (raster), To TIN

Temporal resolution / update frequency years, Temporal resolution / update frequency months, Monitoring applications, change detection

Data Acquisition, Storage / Management, Analysis / Simluation, Visualization / Interaction

More than 1 billion (10^9) points

Potree, LAStools, Esri ArcGIS

ariel.blanco@coe.upd.edu.ph

university of the philippines

Airborne LiDAR, Terrestrial Lidar (including Mobile Mapping), Indoor Laser Scanning, Photogrammetry, SONAR (single and multi-beam echo’s)

Visualization, Digital Terrain Modelling, Feature Extraction, GIS

Forestry, Hydrology, Water Management

In a file on a computer, In a file on a network drive, In a database

Timestamp, Intensity, Colour, Classification, Pulse Form, Pulse Count, Direction and Length of Scanline

To regular grid (raster), To TIN, To features (vector object rafter detection/recognition), None, direct use of point clouds

Temporal granularity at data set (a ‘point cloud’) level, Temporal resolution / update frequency years, Temporal resolution / update frequency months, Monitoring applications, change detection

Storage / Management, Combining Data from multiple source

Data Model, File Format / Encoding

More than 1 billion (10^9) points

bkruse@kruseimaging.com

Airborne LiDAR, Terrestrial Lidar (including Mobile Mapping), Photogrammetry, SONAR (single and multi-beam echo’s)

LAS (ASPRS), LAZ, Flat table

Visualization, Digital Terrain Modelling, Feature Extraction, GIS

In a file on a computer, In a file on a network drive

Timestamp, Intensity, Colour, Classification, Pulse Count, Direction and Length of Scanline

To regular grid (raster), To features (vector object rafter detection/recognition)

Temporal granularity at point level, Temporal granularity at data set (a ‘point cloud’) level, Temporal resolution / update frequency years, Monitoring applications, change detection

Storage / Management, Combining Data from multiple source

Data Model, File Format / Encoding

More than 1 billion (10^9) points

PDAL, LAStools, GRASS, Esri ArcGIS, PCL

cameron.wallace@snclavalin.com

Airborne LiDAR, Terrestrial Lidar (including Mobile Mapping), Photogrammetry

LAS (ASPRS), ASCII, CSAR, PostgreSQL-PostGIS PCPATCH, Flat table

LAS (ASPRS), ASCII, CSAR, PostgreSQL-PostGIS PCPATCH, Flat table

Visualization, Digital Terrain Modelling, Feature Extraction, GIS

Forestry, Hydrology, Water Management

In a file on a computer, In a file on a network drive, In a database

Timestamp, Intensity, Classification

To regular grid (raster), To TIN, To features (vector object rafter detection/recognition), None, direct use of point clouds

Temporal granularity at data set (a ‘point cloud’) level, Temporal resolution / update frequency days

Storage / Management, Combining Data from multiple source, Dissemination, Visualization / Interaction

More than 100 million (10^6) points

Esri ArcGIS, PosgreSQL/PostGIS, CARIS

open compressed standards would be great. I'm primarily a consumer of data at this point, although we may start producing some in the future.

Environmental consulting and technology Inc.

Visualization, Digital Terrain Modelling, Feature Extraction, GIS

In a file on a network drive, Moving towards PostgreSQL with lidar extension

Timestamp, Intensity, Classification

To regular grid (raster), Level of Detail (LoD) representation

Temporal granularity at data set (a ‘point cloud’) level, Temporal resolution / update frequency years

More than 1 billion (10^9) points

PDAL, LAStools, GRASS, Esri ArcGIS, MCC lidar

Please help keep lidar formats open. I've been a geospatial analytical professional for almost 20 years. I also administer Linux systems and heavily use I also use FOSS. I am on the analytical use and data distribution side. Few things are as frustrating as dealing with proprietary data formats. They are an obstacle to the progress of research and knowledge. They prevent sharing of data. Lidar, like other geospatial data, best serves the global community by openly accessible formats.

Ben@connected2fiber.com

Airborne LiDAR, Terrestrial Lidar (including Mobile Mapping)

PostgreSQL-PostGIS PCPATCH

PostgreSQL-PostGIS PCPATCH

Timestamp, Classification

To features (vector object rafter detection/recognition)

Temporal granularity at point level

Data Acquisition, Dissemination

More than 100 million (10^6) points

Shanmugam Ganeshkumar

Airborne LiDAR, Terrestrial Lidar (including Mobile Mapping)

LAS (ASPRS), ASCII, PostgreSQL-PostGIS PCPATCH, PDAL-generated blocks

Visualization, Digital Terrain Modelling, GIS

Smart City, Asset Management

In a file on a network drive, In a database

Timestamp, Intensity, Colour, Direction and Length of Scanline

Level of Detail (LoD) representation

Temporal granularity at point level, Temporal resolution / update frequency months, Temporal resolution / update frequency days

Storage / Management, Combining Data from multiple source, Analysis / Simluation, Visualization / Interaction

Data Model, File Format / Encoding, DBMS / SQL, Web Service (WxxS) protocol

Less than 100 million (10^6) points

PDAL, LAStools, PosgreSQL/PostGIS

Please keep the format open. Don't like esri approach.

Shanmugam Ganeshkumar

Airborne LiDAR, Terrestrial Lidar (including Mobile Mapping)

LAS (ASPRS), ASCII, PostgreSQL-PostGIS PCPATCH, PDAL-generated blocks

Visualization, Digital Terrain Modelling, GIS

Smart City, Asset Management

In a file on a network drive, In a database

Timestamp, Intensity, Colour, Direction and Length of Scanline

Level of Detail (LoD) representation

Temporal granularity at point level, Temporal resolution / update frequency months, Temporal resolution / update frequency days

Storage / Management, Combining Data from multiple source, Analysis / Simluation, Visualization / Interaction

Data Model, File Format / Encoding, DBMS / SQL, Web Service (WxxS) protocol

Less than 100 million (10^6) points

PDAL, LAStools, PosgreSQL/PostGIS

Please keep the format open. Don't like esri approach.

john.armston@dsiti.qld.gov.au

Airborne LiDAR, Terrestrial Lidar (including Mobile Mapping)

Digital Terrain Modelling, Feature Extraction

In a file on a computer, Tape mass storage connected to a HPC

Timestamp, Intensity, Colour, Classification, Pulse Form, Pulse Count, Direction and Length of Scanline, Waveform derived return metrics

To regular grid (raster), None, direct use of point clouds

Temporal granularity at data set (a ‘point cloud’) level, Temporal resolution / update frequency years, Temporal resolution / update frequency months, Monitoring applications, change detection

Data Acquisition, Combining Data from multiple source

More than 1 billion (10^9) points

PDAL, Potree, LAStools, PosgreSQL/PostGIS, spdlib; pylidar

claudia.knoor@bezreg-koeln.de

Digital Terrain Modelling, Feature Extraction

In a file on a network drive

To regular grid (raster), None, direct use of point clouds

More than 1 billion (10^9) points

sanjosealbacete@yahoo.es

Airborne LiDAR, RADAR (PS-InSAR)

Visualization, Digital Terrain Modelling, Feature Extraction, GIS

In a file on a computer, In a file on a network drive

Intensity, Classification, Direction and Length of Scanline

To features (vector object rafter detection/recognition), None, direct use of point clouds

Monitoring applications, change detection

Combining Data from multiple source

Data Model, File Format / Encoding

Less than 100 million (10^6) points

LAStools, GRASS, Esri ArcGIS, TerraSolid, FME

andreas.rostad@kartverket.no

Norwegian Mapping Authority

Visualization, Digital Terrain Modelling, GIS, Distribution to users.

In a file on a network drive, In the cloud

Timestamp, Intensity, Classification

None, direct use of point clouds

Temporal granularity at point level, Temporal granularity at data set (a ‘point cloud’) level, Temporal resolution / update frequency years, Temporal resolution / update frequency months, Temporal resolution / update frequency days, Temporal resolution / update frequency seconds

Storage / Management, Combining Data from multiple source, Change Reference System

Data Model, File Format / Encoding

More than 1 billion (10^9) points

marcin.sologuba@bialystok.lasy.gov.pl

Regional Directorate of The State Forests in Bialystok

Digital Terrain Modelling, GIS

To regular grid (raster), To features (vector object rafter detection/recognition)

Temporal resolution / update frequency years

Combining Data from multiple source, Visualization / Interaction

Data Model, File Format / Encoding

More than 100 million (10^6) points