OPALS

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OPALS

Opals logo 72dpi.jpg
Opals commandprompt.jpeg
OPALS
Orientation and Processing of
Airborne Laser Scanning data
Basic data

developer Institute for Photogrammetry and Remote Sensing (Department of Geodesy,
TU Vienna )
Publishing year 2010
Current  version 2.3.1
( April 23, 2018 )
operating system Windows
Linux (restricted use)
programming language C ++
Python
MATLAB
category ALS data processing
software
License
chargeable with exceptions
OPALS

OPALS ( acronym for Orientation and Processing of Airborne Laser Scanning data) is a modularly structured software for the processing and preparation of Airborne Laser Scanning ( ALS ) data sets. The program was developed at the Institute for Photogrammetry and Remote Sensing in the Department of Geodesy and Geoinformation at the Vienna University of Technology and published in 2010.

Areas of application

The general goal of OPALS is to enable a seamless data processing chain for ALS data. OPALS is a software for the processing of point clouds . This processing chain includes waveform decomposition, georeferencing , quality control, structure line extraction, point cloud classification, the creation of terrain models and specific areas of application. These are predominantly in geomorphology , forestry , hydrology (especially water management ), urban research (e.g. modeling) and energy management (e.g. power lines). Using algorithms , for example, automatic derivation of the terrain (e.g. slope, slope direction), coastlines, individual buildings or trees, tree heights and other forestry or energy management parameters are collected. OPALS enables the processing of large volumes of data . This does not allow interactive dialog processing , but requires batch processing . There is therefore no graphical user interface .

OPALS processes the point clouds under the assumption of a Cartesian coordinate system , which means that the user has to take this into account when importing the data. The interpretation of the results, which strongly depend on the quality of the input data, is also completely up to the user. The automatic logging during the work process, which provides documentation of the entire work process, is particularly important for monitoring application areas, since the work steps of the data processing must be completely identical at a later point in time in order to ensure an exact comparison.

Structure of the software

overview

The Institute for Photogrammetry and Remote Sensing at the Vienna University of Technology has been processing airborne laser scanning ( ALS ) data sets since the mid-1990s . In 2010 the software OPALS was created, before that the DTM ( digital elevation model ) software SCOP ++ was developed. OPALS provides complete processing steps for ALS data sets and handles these using several modules. Selected modules and their functions are listed below as examples.
The input with OPALS can be done in three different ways:

  1. Command prompt / command line in Windows
  2. Python shell
  3. C ++ program

Modules

OPALS contains different modules that are used for different applications. The individual modules can be freely combined like building blocks and thus enable user-specific work. In addition, OPALS also provides packages that contain already grouped modules for special areas of application.
The modules include, for example:

  • Algebra: Generates a new raster data set from the combination of several input rasters using mathematical arithmetic operations.
  • Grid: Allows the derivation of digital terrain models ( DTM ) using simple interpolation techniques such as nearest neighbor or moving planes .
  • StatFilter: Allows the use of different filters taking into account different geometric neighborhood relationships (e.g. smoothing a grid surface).

Other important modules are Import , Shade , ZColor etc.

functionality

OPALS data processing

The data processing process in OPALS begins with the analysis of the raw data from the ALS recordings and a derivation of the 3D point clouds . The recording path is reconstructed and analyzed using the recording echo and any existing GNSS and IMU data. At the same time, georeferencing of all recording points can also take place. A subsequent quality check checks, among other things, the complete data coverage as well as the minimum distance between different points and analyzes how precisely the individual images were superimposed. From the quality control, in turn, new 3D point clouds emerge, which are generated in a final version, the version that meets the highest quality criteria. Various relevant GIS products such as digital terrain models or TINs can be generated from the data obtained in this way.

OPALS data manager

The OPALS data manager (ODM for short) is the core component of OPALS, which enables very large amounts of data from ALS data sets to be processed quickly and efficiently. Here, point sets in the order of magnitude of 10 9 can be dealt with at once.
In addition, due to the spatial index approach of the data manager, geometric data are treated separately from one another. Points and more complex objects are each divided into their own spatial indices, which further increases the processing power. Point data are indexed in a so-called kd-tree , polygon data in an R-tree . In addition to the spatial index structure, the ODM offers the option of storing any attributes of individual geometric objects like in a database. A distinction is made between the following attributes :

  • Predefined attributes (attributes with semantics) which have a fixed name and fixed data types and
  • User-defined attributes (attributes without semantics) which OPALS stores internal irrelevant information or non-predefined attributes.

The OPALS data manager combines the simplicity and efficiency of file-based processing with the flexibility and expandability of database systems .

Supported data formats

OPALS supports a variety of data formats ( vector and raster formats ) that can be read into the software.

Supported vector formats

Compatible vector formats include:

  • LAS : This is an open binary data format of the American Society for Photogrammetry and Remote Sensing tomap3D data from point clouds .
  • SHP : A geodata format from ESRI , which consists of at least three files (geometry, factual and attributedata).

Other supported vector data formats include WNP , XYZ , XYZ , SDW , FWF etc.

Supported raster formats

The compatible raster formats include:

Other supported raster data formats include SCOP , USGSDEM , SDTS , ENVI , NITF , PNG , BMP etc.

further reading

Software concept:

  • G. Mandlburger, J. Otepka, W. Karel, B. Wöhrer, W. Wagner, N. Pfeifer: OPALS (Orientation and Processing of Airborne Laser Scanning data) - Concept and application examples of a scientific laser scanning software. In: G. Kohlhofer, M. Franzen (ed.): Publications of the German Society for Photogrammetry, Remote Sensing and Geoinformation e. V. Lectures on the three-country conference OVG, DGPF and SGPF. Vienna 2010, pp. 376–387.
  • J. Otepka, G. Mandlburger, W. Karel: The OPALS data manager-efficient data management for processing large airborne laser scanning projects. In: ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Volume 1-3, 2012, pp. 153-159. doi: 10.5194 / isprsannals-I-3-153-2012
  • N. Pfeifer, G. Mandlburger, J. Otepka, W. Karel: OPALS - A framework for Airborne Laser Scanning data analysis. In: Computers, Environment and Urban Systems. (45) 2013, pp. 125-136. doi: 10.1016 / j.compenvurbsys.2013.11.002

Use cases:

  • L. Eysn, M. Hollaus, K. Schadauer, N. Pfeifer: Forest Delineation Based on Airborne LIDAR Data. In: Remote Sensing. 4 (3) 2012, pp. 762-783. doi: 10.3390 / rs4030762
  • L. Eysn, N. Pfeifer, C. Ressl, M. Hollaus, A. Grafl, F. Morsdorf: A Practical Approach for Extracting Tree Models in Forest Environments Based on Equirectangular Projections of Terrestrial Laser Scans. In: Remote Sensing. 5 (11) 2013, pp. 5424-5448. doi: 10.3390 / rs5115424
  • M. Harzhauser, A. Djuricic, O. Mandic, M. Beschin, P. Dorninger, C. Nothegger, B. Székely, E. Puttonen, G. Molnár, N. Pfeiffer: Disentangling the history of complex multi-phased shell beds based on the analysis of 3D point cloud data. In: Palaeogeography, Palaeoclimatology, Palaeoecology. 437, 2015, pp. 165-180. doi: 10.1016 / j.palaeo.2015.07.038
  • B. Höfle, L. Griesbaum, M. Forbriger: GIS-Based Detection of Gullies in Terrestrial LiDAR Data of the Cerro Llamoca Peatland (Peru). In: Remote Sensing. 5 (11), 2013, pp. 5851-5870. doi: 10.3390 / rs5115851
  • M. Hollaus, G. Mandlburger, N. Pfeifer, W. Mücke: Land cover dependent derivation of digital surface models from airborne laser scanning data. In: N. Paparoditis, M. Pierrot-Deseilligny, C. Mallet, O. Tournaire (eds.): ISPRS Commission III Symposium. PCV 2010. Photogrammetric Computer Vision and Image Analysis. Saint-Mandé 2010, pp. 221-226.
  • G. Mandlburger, C. Hauer, B. Höfle, H. Habersack, N. Pfeifer: Optimization of Lidar derived terrain models for river flow modeling. In: Hydrology and Earth System Sciences. 13, 2009, pp. 1453-1466. doi: 10.5194 / hess-13-1453-2009

Individual evidence

  1. ↑ https://opals.geo.tuwien.ac.at/html/stable/usr_install.html Installation instructions OPALS. Retrieved April 9, 2019.
  2. ↑ https://opals.geo.tuwien.ac.at/html/stable/usr_faq.html OPALS FAQ. Retrieved April 9, 2019.
  3. ↑ https://rs.geo.tuwien.ac.at/ Remote sensing group TU Vienna. Retrieved February 18, 2017.
  4. ↑ https://opals.geo.tuwien.ac.at OPALS homepage. Retrieved April 9, 2019.
  5. ^ N. Pfeifer et al .: OPALS - A framework for Airborne Laser Scanning data analysis. 2013, p. 134.
  6. ^ N. Pfeifer et al .: OPALS - A framework for Airborne Laser Scanning data analysis. 2013, p. 134.
  7. ↑ https://photo.geo.tuwien.ac.at/software/scop// SCOP ++. Retrieved February 18, 2017.
  8. ↑ https://opals.geo.tuwien.ac.at OPALS Packages. Retrieved April 9, 2019.
  9. ↑ https://opals.geo.tuwien.ac.at/html/stable/ModuleAlgebra.html Module Algebra. Retrieved April 9, 2019.
  10. ↑ https://opals.geo.tuwien.ac.at/html/stable/ModuleGrid.html Module Grid. Retrieved April 9, 2019.
  11. ↑ https://opals.geo.tuwien.ac.at/html/stable/ModuleStatFilter.html Module StatFilter. Retrieved April 9, 2019.
  12. ↑ https://opals.geo.tuwien.ac.at/html/stable/ref_moduleTable.html OPALS modules. Retrieved April 9, 2019.
  13. ↑ https://opals.geo.tuwien.ac.at/html/stable/ref_odm.html#ref_odm_spatial OPALS data manager - spatial index. Retrieved April 9, 2019.
  14. ^ N. Pfeifer et al .: OPALS - A framework for Airborne Laser Scanning data analysis. 2013, p. 130.
  15. ↑ https://opals.geo.tuwien.ac.at/html/stable/ref_odm.html#ref_odm_db OPALS data manager - database function. Retrieved April 9, 2019.
  16. ^ N. Pfeifer et al .: OPALS - A framework for Airborne Laser Scanning data analysis. 2013, p. 130.
  17. ↑ https://opals.geo.tuwien.ac.at/html/stable/usr_supported_fmt.html Supported data formats. Retrieved April 9, 2019.
  18. ↑ https://www.asprs.org/committee-general/laser-las-file-format-exchange-activities.html LAS data format. Retrieved February 18, 2017.
  19. ↑ https://www.asprs.org/ ASPRS. Retrieved February 18, 2017.
  20. ↑ http://www.esri.com/library/whitepapers/pdfs/shapefile.pdf ESRI Shapefile technical description. Retrieved February 18, 2017.