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State of the Art and Future Trends in Geoinformatics

State of the Art and Future Trends in Geoinformatics. Gerhard Navratil navratil@geoinfo.tuwien.ac.at. Contents. How to determine State of the Art? GIS: The Early Years Framework Changes Changes in Research Questions Future Challenges.

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State of the Art and Future Trends in Geoinformatics

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  1. State of the Art and Future Trends in Geoinformatics Gerhard Navratil navratil@geoinfo.tuwien.ac.at

  2. Contents • How to determine State of the Art? • GIS: The Early Years • Framework Changes • Changes in Research Questions • Future Challenges Gerhard Navratil

  3. How to Determine State of the Art?How to Determine Future Trends? • Look at industry solutions? • Look at publications in journals? • Look at presentations in conferences? • Look at the development of knowledge!Try to extrapolate! Gerhard Navratil

  4. GIS: The Early Years 1960‘s: First Steps of GIS • Computers slow • Storage media slow andexpensive (tapes) • No graphical out put Nixdorf 820, 1968 (Christian Giersing ) Gerhard Navratil

  5. Early Maps (Marble et al. 1984) Gerhard Navratil

  6. Early Topics • Data storage • Networks and topology • Attribute modelling • Required functionality • User interface • Graphical output Gerhard Navratil

  7. Example: Geometry • Representation • Vector: Spaghetti, Topology (1980‘s) • Raster: Simple concept, easy to print, scanned maps • Efficient storage • Databases save space (relational DB) (Codd 1969) • Problems of data combination • Map algebra (Tomlin, 1990) • Line intersection problem Gerhard Navratil

  8. Example: Line Intersection • It Makes Me so Cross (Douglas, 1974) • Task: General purpose FORTRAN routine to decide if two line segments intersect • 5 pages of text, 21 special cases • It Doesn‘t Make Me Nearly as Cross (Saalfeld, 1987) • New representation (point-vector) • determine r, r' – intersect if both in [0,1] Gerhard Navratil

  9. What Happened? Implementation led to problems First solution e-improvements? Improvement by different approach More elegant solution Gerhard Navratil

  10. Framework Changes (80‘s/90‘s) • Increasing amount of computing power (from exclusive equipment to ubiquitous infrastructure) • Standard graphical user interfaces • GIS on standard office PC‘s Gerhard Navratil

  11. Problem: Data Supply Main data sources: • Scanned maps (outdated) • Measurements (slow, expensive) • Satellite images (low resolution, expensive) • Aerial photography (required digitizing, expensive)  Standard Data Suppliers (e.g., Ordnance Survey) Gerhard Navratil

  12. Advantages of Standard Data Sources • Well developed data capture processes  known quality • Clear understanding of the limits of the data • (At least some) Liability issues solved Gerhard Navratil

  13. Disadvantages of Standard Data Sources • Standard products with defined quality – only limited options • Dependency on a single data provider • Market power of producers  Data quality discussed from producer perspective only Gerhard Navratil

  14. Software Small number of commercial GIS: • ESRI • Intergraph • Siemens • MapInfo • (Erdas) Almost no independent products (mainly GRASS and Spring) Gerhard Navratil

  15. Recent Changes • New communication technology (Internet, mobile phones, WLAN) • Abundant data: • Volunteered Geographic Information (VGI) • Satellite images • Laser Scanning/Digital Photogrammetry • Software producing communities (open source software) Gerhard Navratil

  16. New Tools/Environments • GNSS: Positioning information is availablehigh level of quality • Smartphones (mobile, bi-directional access to data) • Google Earth, Google Maps, Microsoft Bing Gerhard Navratil

  17. Changes in Research Questions • Quality of the new data? • Users are no experts  Communication with lay people • Data used during execution of a process, not during planning – changes? Gerhard Navratil

  18. Research Questions on Data (1) • Understanding the processes that produce the data • Quality checks? Consistency? Updates? • Data processing steps? • Understanding the communities providing the data • What is the incentive? • What is the task for which the data is needed? • Knowledge level of data producers? Gerhard Navratil

  19. Research Questions on Data (2) • Limitations of the data set? • Scale of the data capture? • What is the quality? Is it uniform? • Connection between different data sets? • Different communities collecting similar data in the same region? • Similar communities collecting similar data in neighbouring region? Gerhard Navratil

  20. Research Questions on Users • What is the information needed by the user? • Required level of quality? • Required additional information? • How to best communicate the information? • Graphical or Verbal or Oral? • User-oriented or as a map? • Level of redundancy? Gerhard Navratil

  21. Example: OpenStreetMap (1) • Data provided by • Communities • Organizations (e.g., Ordnance Survey) • Private persons • Data collected by • GPS-tracks • Digitizing aerial images Teheran (OSM, 2011) Gerhard Navratil

  22. Public Transport in Berlin (Melchior Moos, 2008) Example: OpenStreetMap (2) • Free to use (License: Creative Commons) • Usable for routingand mapping • Available forlarge parts of theworld Gerhard Navratil

  23. Example: OpenStreetMap (3) • User tasks • Cartography (professionals/amateurs) • Navigation (routing) • Assessing the quality is difficult • Attribute accuracy in international context? • Completeness?In comparison to what? NAVTEQ/TeleAtlas-data? Gerhard Navratil

  24. Example: OpenStreetMap (4) Classification in different countries,e.g., highway = tertiary (Wikipedia) (Google Earth) (Wikipedia) Gerhard Navratil

  25. Emerging Research Fields • Semantics of data • Assessment of data quality for VGI • User interfaces • Processes and time Gerhard Navratil

  26. (Comber, 2007) Semantics of Data (1) Data from different sources – what happens when we combine them? • Different communities use different classifications – land cover vs. land use? • Comparing apples andoranges? (Wikipedia) Gerhard Navratil

  27. Semantics of Data (2) Current tool: Ontologies Research questions: • Semantics of processes • Vagueness • Translation of terms between domains • Trust in semantic quality of VGI Gerhard Navratil

  28. Assessment of Data Quality (1) • Easy for result of single observation (quality of equipment) • Difficult if • Data collected during extended periode.g., land management • Data collected by vast number of peoplee.g., VGI Gerhard Navratil

  29. Assessment of Data Quality (2) Ideas for quality assessment in land management: • Geometrical quality of cadastral boundaries: Compare data set with original surveys(Navratil et al. 2010) • Compare the data sets with orthophotos Result: • Varying quality – how to communicate? • A: deviations between a few cm and 150m Gerhard Navratil

  30. User Interfaces • New impulses for interfaces from Google Earth, smartphones, etc.How to exert this? • How to exploit the new hardware?e.g., smartphones, tablets • 2D or 3D? When to use what? • Virtual reality or mixed reality? Applications? Benefits? Realization? Gerhard Navratil

  31. Processes and Time (1) • Data are not static – reality changes constantly  Data are connected to the date of collection • Data describe/are influenced by processese.g., sensor networks • Consistency checks require combination of processes and datae.g., differential equations (Hofer & Frank 2009) Gerhard Navratil

  32. Processes and Time (2) Task are described by • Location • Duration • Prerequisites Coordination of tasks requires • Start and end location of tasks • Duration of navigation between different locations Gerhard Navratil

  33. Conclusions (1) Finding research topics requires • Understand the recent developments • Detect changes in the framework • Find the consequences of these changes • Look for missing links Gerhard Navratil

  34. Conclusions (2) Future key research topics are • Semantics of data • Assessment of data quality for VGI • User interfaces • Processes and time Gerhard Navratil

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