Abstract
Several international studies attempt to construct tangible GIS systems, forming real 3D surfaces using a large number of mechanical parts along a matrix formation. Most of these attempts suffer in cost, accuracy, resolution and/or speed. In order to facilitate data handling in GIS tangible systems, the generalization process becomes crucial, accommodating compression, visualization and comprehension of spatial data under various scales. Under this perspective, the main objective of the proposed adaptive generalization approach is to provide optimized representation of 3D digital terrain models with minimum loss of information, serving specific applications. That is, to minimize the number of pixels in a raster dataset used to define a DTM, while reserving surface information and enhancing the important semantic features. To this aim, this paper presents specific computationally efficient surface generalization approaches, which can be incorporated in applications of tangible GIS systems, due to their low processing time, facilitating real-time results. The research methods developed and tested include adaptive variations of a) the Douglas-Peucker line simplification algorithm in 3D data and b) the spatial Laplace filter that estimates the significance of each node, based on its nearest neighbors. The proposed strategy also dynamically incorporates topology and semantic restraints, in order to preserve important information and depict it with variable detail level, according to the application at hand. The algorithms are evaluated on their accuracy for a specific wildfire application and for different reduction levels in two different types of terrain data. They are also compared to their original-basic implementations in terms of performance.
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References
Akel NA, Kremeike Katrin, Filin Sagi, Sester Monika, Doytsher Yerach (2005) Dense DTM Generalization Aided by Roads Extracted from LiDAR Data", ISPRS WG III/3, III/4, V/3 Workshop "Laser scanning 2005″, Enschede, the Netherlands, September 12–14
Bakuła K (2011) Comparison of six approaches in DTM reduction for flood risk determination. Challenges of Modern Technology 2(4):31–36
Brassel KE, Weibel R (2007) A review and conceptual framework of automated map generalization. Int J Geogr Inf Syst 2(3):229–244
Douglas DH, Peucker TK (1973) Algorithms for the reductions of the number of points required to represent a digitised line or its caricature. The Canadian Cartographer 10(2):112–122
Douglas D, Peucker T (1975) "detection of surface-specific points by local parallel processing of discrete terrain elevation data", Computer Graphics. Visions and Image Processing 4(2):375–387
Forberg A (2004). Generalization of 3D Building Data Based on a Scale-space Approach The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XXXV(B), 194–199
Gonzalez CR, Woods ER (1992) Digital image processing. Addison-Wesley, Boston
Heckbert PS, Garland Michael (1997). Survey of Polygonal Surface Simplification Algorithms, Tech. report, Carnegie Mellon University
Kremeike K (2004) Generalization of dense digital terrain models while enhancing important objects. International Archives of Photogrammetry and Remote Sensing 35(B4):403–408
Lang T (1969) Rules for robot draughtsmen. Geogr Mag 42:50–51
Leithinger D, Ishii Hiroshi, (2010). Relief: A Scalable Actuated Shape Display In Proceedings of the fourth international conference on Tangible, embedded, and embodied interaction (TEI '10). ACM, New York, USA, 221–222
Li Z, Openshaw S (1992) Algorithms for automated line generalization based on a natural principle of objective generalization. Int J Geogr Inf Syst 6(5):373–389
Mandlburger G, Hauer C, Hofle B, Habersack H, Pfeifer N (2008) Optimisation of LiDAR derived terrain models for river flow modelling. Hydrol Earth Syst Sci Discuss 5:3605–3638
Martín MT, Jaime Rodríguez, Jesús Irigoyen, Pedro Arias, (2009). Structural Parameters For Hybrid DTM Generalization, Proceedings of the 24th International Cartographic Conference Santiago de Chile, Chile, 15–21, November
Mayer H, (2000). Scale-Space Events For The Generalization Of 3d–Building Data, International Archives of Photogrammetry and Remote Sensing. Vol. XXXIII, Part B4. Amsterdam
McMaster RB, Shea KS (1992). Generalization in digital cartography, Washington DC, Association of American Geographers
Opheim H (1982) Fast data reduction of a digitized curve. Geo-Processing 2:33–40
Pajarola R., Antonijuan Marc, Lario Roberto, (2002). QuadTIN: Quadtree based triangulated irregular networks, In Proceedings IEEE Visualization '02, Boston, 27 Oct. - 1 Nov. 2002, pp. 395–402
Partsinevelos P., Papadogiorgaki Maria, (2014). Digital terrain model generalization incorporating scale, semantic and cognitive constraints, European Geosciences Union General Assembly 2014 (EGU2014), 27 April – 02 May
Ratti C, Wang Y, Ishii H, Piper B, Frenchman D (2004) Tangible user interfaces (TUIs): a novel paradigm for GIS. Trans GIS 8(4):407–421
Reumann K, Witkam APM (1974) Optimizing curve segmentation in computer graphics. In: Proceedings of international computing symposium. North-Holland Publishing Company, Amsterdam, pp 467–472
Santo M. D., Guilherme Wosny, Francisco de Oliveria, (2009). Algorithms for Automated Line Generalization in GIS, 2008 ESRI User Conference Proceedings
Sester M., (2007). 3D Visualization and Generalization, Photogrammetric Week 07, Wichmann, 03.09–07.09.2007. Stuttgart, Germany, 285–295
Shea KS, McMaster RB (1989) Cartographic generalization in a digital environment: when and how to generalize. Auto-carto 9 Proc symposium, Baltimore, MD 1989:56–67
Shi W, Cheung CK (2006) Performance evaluation of line simplification algorithms for vector generalization. Cartogr J 43(1):27–44
Thapa K (1989) Data compression and critical points detection using normalized symmetric scattered matrix. Auto-carto 9 Proc symposium, Baltimore, MD 1989:78–89
Visvalingam M, Whyatt JD (1993) Line generalization by repeated elimination of points. Cartogr J 30:46–51
Weibel R (1997) Chapter 5. Principles generalization of spatial data: and selected algorithms. Algorithmic Foundations of Geographic Information Systems, Lecture Notes in Computer Science 1340:99–152
Zhao Z., Saalfeld A., (1997). L1inear-time sleeve-fitting polyline, in Autocarto 13, ACSM/ASPRS’97 Technical Papers, Seattle, Washington, Vol. 5: 214–223, April
Acknowledgements
This study has been performed under the framework of the “Cooperation 2011” project ATLANTAS (11_SYN_6_1937) funded from the Operational Program “Competitiveness and Entrepreneurship” (co-funded by the European Regional Development Fund (ERDF)) and managed by the Greek General Secretariat for Research and Technology. Also, we would like to acknowledge the National Cadastre and Mapping Agency of Greece for freely providing the Digital Elevation Models and corresponding satellite orthorectified imagery.
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Communicated by: H. A. Babaie
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Papadogiorgaki, M., Partsinevelos, P. Adaptive DTM generalization methods for tangible GIS applications. Earth Sci Inform 10, 483–494 (2017). https://doi.org/10.1007/s12145-017-0311-9
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DOI: https://doi.org/10.1007/s12145-017-0311-9