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USES OF HIGH-RESOLUTION IMAGERY FOR URBAN TRANSPORTATION 
APPLICATIONS: QUANTITATIVE INDICES EXTRACTION APPROACHES 
K.Lee?, K.- H. Chi? 
“Dept. of Information Systems, Hansung University, Seoul, 136-792, KOREA - kilec@hansung.ac kr 
? KIGAM( Korea Institute of Geoscience and Mineral Resources), Daejeon-city, 305-350, KOREA 
- khchi@rock25t.kigam.re.kr 
KEY WORDS: Extraction, High resolution, GIS, Indicators, Urban 
ABSTRACT: 
Recently, new approaches with commercial high-resolution satellite imagery in the engineering application domains have been 
attempted. Among them, uses of remotely sensed imagery linked with GIS-T (Geographic Information Systems for Transportation) 
or transportation geography are regarded as one of prospecting issues. As the matter of facts. most transportation applications are 
needed real-time or near real-time processing for data acquisition, analysis, or broadcasting; comparatively, uses of remotely sensed 
imagery in this field are somewhat oriented to periodic change detection and analysis, prediction and forecasting. Even in this 
approach, high-resolution imagery is more advantageous than coarse and medium resolution imagery. Normally, two kinds of 
approaches are prevailed such as image recognition/interpretation and automatic or semi-automatic feature extraction. With this base 
considerations and engineering viewpoints, practical uses of remote sensed imagery in urban transportation environment analyses 
were carried out with newly implemented extension programs running under desktop GIS environment in this study: Connectivity 
index and Circuity index, which mean degree of connectivity and degree of circuit in a network, respectively. As for connectivity 
index, three types of algorithms such as alpha, gamma, and shimbel index were implemented, and extraction program of circuity and 
accessibility matrix were implemented with OD (Origin-Destination) matrix computation used in travel demand analysis in 
transportation geography. In both cases, high-resolution imagery is used in determination of user-defined arbitrary analysis zone or 
AOI (Area Of Interest), corresponding to TAZ (Traffic Analysis Zone) in GIS-T and real-time GIS database updating such as road 
boundary, centerline and other target features on the scene. Therefore, after user selects AOI and updates database, these indices can 
be easily computed using implemented program in this study. By this approach, new quantitative information to characterize an 
urban transportation environment in a certain region can be easily obtained and utilized as meaningful indicators related to 
transportation planning process or urban planning, comparing with those results produced without high-resolution imageries. 
1. INTRODUCTION information to delineate a given network structure, and shimbel 
index, circuity/connectivity/accessibility in the matrix form. 
Especially, these GIS-based spatial metrics are known to 
As various types of engineering applications dealing with provide useful quantitative information for urban transportation 
geo-spatial imagery such as commercial uses of high-resolution 
satellite imagery are possible, analytical GIS-based technology 
on geo-spatial imagery has been studied. Utility of geo-spatial 
imagery in the applications for urban transportation analysis, 
which often refers to GIS-T (GIS for Transportation) and GIS 
network analysis functionalities, is regarded as one of these 
approaches (Khuen, 1997; Lang, 1999; Donnay et al., 2001; 
Miller and Shaw, 2001). Most analytical functions in GIS-based 
network analysis are based on problem-solving methodology in 
the transportation geography (Han, 1996; Chou, 1999). 
Lo and Yeung(2002) summarized that there are two main 
groups in measures for network analysis in the geography: one 
is to extract overall characteristic based on a topological graph 
or topological graphs, and the other is to compute the shortest 
or optimal path finding and allocation segments. Currently, 
most commercial geo-processing software systems provide 
network analysis modules. However, feasible functions to 
extract basic quantitative indices for transportation network 
structure in a certain region are rare in those systems. Recently, 
some studies to implement fundamental functions using geo- 
spatial imagery based on GIS have been carried out and 
tentatively tested (Lee, 2002; Lee ef al., 2003). 
Main focuses in this study are on implementation to extract 
basic connectivity indices related to transportation network: 
alpha index and gamma index, which are known as fundamental 
analysis, and each index provides individual significance to 
interpret a given network structure. Geo-spatial imagery 
including high-resolution imagery or digitally processed 
airborne photograph can also be effectively used as a base 
image in these applications. 
In this study, an extension program for automatic 
computation of those indices is newly implemented in 
Avenue!", as AVX extension programs running on ESRI- 
ArcView® GIS. These extension programs in AVX-complied 
are different from other ones such as (Ormsby and Alvi, 1999) 
and Lee and Wong (2001). On application of this program, it is 
designed that spatial database such as road centerline or 
network structure with nodes and administrative boundary is 
needed as the user-sided minimum requirements. Some case 
studies regarding practical application of these programs are 
presented mainly with KOMPSAT EOC. 
2. TYPES OF QUANTITATIVE INDICES FOR 
TRANSPORTATION ANALYSIS 
2.1 Connectivity Indices and Shimbel index 
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