CMRT09

Stilla, Uwe

Bibliographic data

Monograph

Persistent identifier:: 856955019

Author:: Stilla, Uwe

Title:: CMRT09

Sub title:: object extraction for 3D city models, road databases, and traffic monitoring ; concepts, algorithms and evaluation ; Paris, France, September 3 - 4, 2009 ; [joint conference of ISPRS working groups III/4 and III/5]

Scope:: X, 234 Seiten

Year of publication:: 2009

Place of publication:: Lemmer

Publisher of the original:: GITC

Identifier (digital):: 856955019

Illustration:: Illustrationen, Diagramme, Karten

Language:: English

Usage licence:: Attribution 4.0 International (CC BY 4.0)

Publisher of the digital copy:: Technische Informationsbibliothek Hannover

Place of publication of the digital copy:: Hannover

Year of publication of the original:: 2016

Document type:: Monograph

Collection:: Earth sciences

Chapter

Title:: SURFACE MODELLING FOR ROAD NETWORKS USING MULTI-SOURCE GEODATA Chao-Yuan Lo, Liang-Chien Chen, Chieh-Tsung Chen, and Jia-Xun Chen

Document type:: Monograph

Structure type:: Chapter

In: Stilla U, Rottensteiner F, Paparoditis N (Eds) CMRT09. IAPRS, Voi. XXXVIII, Part 3/W4 — Paris, France, 3-4 September, 2009 SURFACE MODELLING FOR ROAD NETWORKS USING MULTI-SOURCE GEODATA Chao-Yuan Lo , Liang-Chien Chen, Chieh-Tsung Chen, and Jia-Xun Chen Department of Civil Engineering, National Central University, Jhungli, Taoyuan 32001, Taiwan - freezer@csrsr.ncu.edu.tw Center for Space and Remote Sensing Research, National Central University, Jhungli, Taoyuan 32001, Taiwan - lcchen@csrsr.ncu.edu.tw Department of Land Administration, Taipei 10055, Taiwan - {moi5383; moil240}@moi.gov.tw Commission III, WG III/4 KEY WORDS: Surface, Reconstruction, Three-dimensional, Geometric, Laser scanning, Modelling ABSTRACT: Road systems are the fundamental component in the geographic information systems. This kind of civil infrastructures has large coverage and complex geometry. Thus, the modelling process leads to handling huge data volume and multi-source datasets. A reasonable process should be able to reconstruct separate parts of road networks and combine the surfaces together. Hence, the reconstruction of complete three-dimensional road networks needs scrutiny when a large area is to be processed. This paper proposes a scheme to focus on this issue using an integrated strategy with multi-source datasets. The modelling processes combine different data sources to refine road surfaces to keep the continuities in elevation and slope. The proposed scheme contains three parts: (1) data pre-processes, (2) planimetric networking, and (3) surface modelling. In the first part, datasets are registered in the same coordinate system. In the next step, topographic maps provide the roadsides to derive the geometric topology of road networks. Finally, those centerlines combine airborne laser scanning data to derive road surfaces. Considering the data variety, some road segments generated from aerial images are also included in the proposed scheme. Then, the successive process integrates those models for the refinement of road surfaces. The test area is located in Taipei city of Taiwan. The road systems contain local streets, arterial streets, expressways, and mass rapid transits. Some roadways are multi-layer and cross over with different heights. The final results use three-dimensional polylines and ribbons to represent geometric directions and road surfaces. Experimental results indicate that the proposed scheme may reach high fidelity. 1. INTRODUCTION Based on the viewpoint of decision support for modem cities, the reconstruction of a virtual environment is an essential task. The applications include urban planning, traffic simulation, true orthorectification (Zhou et al., 2005), hazard simulation, communication, etc. Since the road models are one of the most prominent components in the urban information systems, the reconstruction of the model becomes increasingly important. In general, the traditional topographic map is a kind of widely used dataset that describes road geometries. It can efficiently build single-layer road models. However, this civil infrastructure is developed rapidly in modem cities for the traffic demand, and road types become more complex including local streets, arterial streets, expressways, freeways, and mass rapid transit. Single-layer road networks have changed to multi-layer systems and topomaps may be insufficient to describe complex roads. The elevation information of road surfaces needs to be considered for the separation of overpasses. Some researches focused on the surface modelling processes with different strategies and data, e.g. aerial photos, laser scanning data, GPS data, topomaps, and so on. Cannon (1992) proposed a scheme to locate the three-dimensional road profiles integrating GPS and INS data. A related work also had been made to estimate the slope information of road profiles using GPS data (Han and Rizos, 1999). Some studies preferred to derive road information in spectral domain. They analyzed road shapes of centerlines or boundaries to derive road geometries with vehicle-based images (Yan et cl., 2008), aerial photos (Treash and Amaratunga, 2000; Hinz and Baumgartner, 2003; Dal Poz et al., 2004), satellite images (Yan and Zhao, 2003; Doucette et al., 2004; Hu et al., 2004a; Kim et al., 2004; Karimi and Liu, 2004; Yang and Wang, 2007), airborne laser scanning data (Clode et al., 2007). Some proposed semi-automatic approaches basing on the matching technique to reliably extract road geometries with manual editing from high-resolution satellite imagery (Hu et al., 2004a; Kim et al.,2004). Easa et al. (2007) focused on the automatic image processing to extract edge lines for calculation of geometric parameters to describe horizontal alignments from high resolution images. On the other hand, an integrating strategy had been proposed to deal with this issue using aerial images and laser scanning data (Hu et al., 2004b; Zhu et al., 2004). Zhang (2003) integrated aerial photos and geo-database to derive and update three- dimensional road data. Moreover, geo-database and laser scanning data also could be a combination. Hatger and Brenner (2003) calculated the profile geometries of centerlines from the geo-database and digital surface models. The segment-based method used region growing to detect road areas for the calculation of geometric parameters to refine the geo-database. Furthermore, Cai and Rasdorf (2008) also combined two datasets, airborne laser scanning data and planimetric centerline Corresponding author

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