Uses of High-resolution Imagery for Urban Transportation Applications: Quantitative Indices Extraction Approaches

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.