Quantitative analysis of transportation network connectivity with road features automatically extracted from high-resolution satellite imagery

Abstract

Potential uses of high-resolution satellite imagery such as KOMPSAT (KOrea Multi Purpose SATellite) EOC (Electro Optical Camera) or IKONOS, in the field of urban planning and transportation planning are widely recognized; currently, actual applications in these fields have been tried in the most countries. However, most approaches, in some extents, are urban feature extraction by automatic or semi-automatic methodologies and human interpretation or recognition with other ancillary digital information. In this study, automatic road feature extraction firstly was attempted with newly implementation of GDPA (Gradient Direction Profile Algorithm), proposed by Wang and Zhang (2000). Further, road features extracted from some ortho-rectified KOMPSAT EOC and IKONOS imageries at a case study area in nearby Seoul, Korea, as results of GDPA, were evaluated with actual digital GIS road layers of National Geographic Institute in Korea, by computation of commission and omission error assessment. Second, these road features, after accuracy assessment, were used to extract quantitative indices such as α index, and γ index, in the area of interests. These indices are for providing information on connectivity status of road network, and also related to accessibility of multi-link structure. Conclusively, it is thought that the results and products in this study can be effectively utilized to local government applications such as urban planning and transportation planning for transportation geographic analysis associated with high-resolution remotely sensed imageries.