Publication: Cartography and Geographic Information Science
Publication Date: 01-JUL-07
Delivery: Immediate Online Access
Author: Filin, Sagi ; Akel, Nizar Abo ; Kremeike, Katrin ; Sester, Monika ; Doytsher, Yerach
Article Excerpt
Introduction
Traditionally, 2D cartography has employed a workflow of different processes (such as aggregation, simplification, enhancement, and displacement) to create the desired cartographic product. However, the digital era we are in offers far greater flexibility in terms of both the available data and potential applications. For example, the availability of dense 3D data offers new possibilities concerning the communication of topographic information, particularly in the context of dynamic and adaptive maps created for instantaneous use. The communication process thus needs to be viewed as a set of individually configurable processes that adapt to the underlying data. Achieving this requires different extraction and interpretation operations (depending on the data), and appropriate generalization operations that conform to the general objective of the communication process.
The research reported here instantiates modules of this communication process applicable for roads extracted from raw 3D LiDAR data, and their cartographic generalization and exaggeration within a 3D terrain model. The communication pipeline for this scenario is demonstrated in Figure 1. The first step is the extraction of spatial information from the point cloud. This information can be in the form of terrain only or a combination of terrain and other topographic features, such as roads. Additional information from external sources, e.g., features from a GIS database, can also be input into this process. The next step is the generalization of the information; one can generalize the terrain, the other topographic features, or both. The choice of generalization processes depends in part on the topography.
[FIGURE 1 OMITTED]
For example, if roads cross relatively flat areas, one can generalize them using such traditional cartographic signatures as overlay of thick lines. For rolling terrain or mountainous areas, traditional cartographic signatures will create a disturbing effect, as they will partly cover the original road and partly the surrounding topography. Therefore, in these areas the generalization of both terrain and road elements becomes significant. The final output is the visualization of the information as a communication tool.
The following sections provide an overview of related work, discussions of the interpretation and generalization of 3D topography, a description of the experimental results from our work, and conclusions.
Related Work
Interpretation of Topographic Features from LiDAR Data
The conversion of raw laser data into a generalized terrain model with explicit road networks requires the detection and the extraction of both entities from the raw point cloud. Extraction of the terrain from the raw laser data has been a subject of intensive research in recent years, leading to a variety of techniques. These can be classified into the following categories: morphological algorithms (e.g., Vosselman 2000), densification based models (Axelsson 1999), analysis of surface discontinuities (Brovelli et al. 2004), models based on robust surface fitting (Briese and Pfeifer 2001), and models based on surface segmentation (Sithole and Vosselman 2003; Nardinocchi et al. 2003). Recently, models that combine individual techniques have been proposed as a means of compensating for the shortcomings of the individual approaches (see Abo-Akel et al. 2004, Tovari and Pfeifer 2005). A comprehensive review of the principal filtering approaches and an analysis of their performance is presented in Sithole and Vosselman (2004). We utilize the filtering model proposed in Abo-Akel et al. (2004) because of its good performance over regions with complex topographic features (see Abo-Akel et al. 2007).
The detection of road networks has been the subject of research involving imagery from air- and spaceborne platforms (see e.g., Wiedemann and Hinz 1999; Hinz and Baumgartner 2003; and most notably the collection of manuscripts in the special issue on road extraction in Photogrammetric Engineering & Remote Sensing 70(12)). Rieger et al. (1999) have proposed road extraction from airborne laser scanning data of forested mountainous areas using breakline detection. Clode et al. (2004) incorporate height and intensity data measured by laser scanners, and Zhu et al. (2004) employed conversion of laser data into range images as an aid to extracting roads in urban areas. Hatger and Brenner (2003) present the extraction of a detailed road description from laser scanning data and existing road network databases. Similarly, Oude Elberink and Vosselman (2006) present an approach for the extraction and 3D modeling of roads based on maps and high-resolution topographic information.
The model proposed here assumes no supplementary information in the form of GIS information or intensity data. Instead, its focus is entirely on 3D geometric modeling and road-network structure reconstruction.
Generalization of Topographic Features
Simplification or generalization processes aim to achieve an acceptable processing time for large datasets and communicate the main features in the data. The high-quality terrain point sets generated from laser altimetry yield a massive amount of data that is difficult to manage in real time, particularly for an efficient graphic representation. Surface simplification techniques have been developed for rendering 3D data, which produce economical surface models and provide powerful tools for tailoring large datasets to the needs of individual applications (Garland 1999). The often-cited survey from Cignoni et al. (1998) gives an overview of existing methods and compares different mesh simplification algorithms. Heckbert and Garland (1997) present a comprehensive summary on polygonal surface simplification, in which they categorize existing algorithms.
Topographic surface models often include objects with a "topographic...
NOTE: All illustrations and photos have been removed from this article.
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