Vehicular ad-hoc networks (VANETs) are highly mobile wireless ad hoc networks for vehicular safety and other commercial applications, whereby vehicles move non-randomly along roads while exchanging information with other vehicles and roadside infrastructures. Inter-vehicle communication (IVC) is achieved wirelessly using multihop communication, without access to fixed infrastructure. Rapid movement and frequent topology changes cause repeated link breakages, increasing the packet loss rate. Geographical routing protocols are suitable for VANETs. However, they select the node nearest to the destination node as a relay node within the transmission range, increasing the possibility of a local maximum and link loss because of high mobility and urban road characteristics. We propose a grid-based predictive geographical routing (GPGR) protocol, which overcomes these problems. GPGR uses map data to generate a road grid and to predict the moving position during the relay node selection process. GPGR divides roads into two-dimensional road grids and considers every possible node movement. By restricting the position prediction in the road grid sequence, GPGR can predict the next position of nodes and select the optimal relay node. Simulation results using ns-2 demonstrated performance improvements in terms of local maximum probability, packet delivery rate, and link breakage rate.

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Image classification is used in various fields such as thematic map making, disaster analysis and so on. Meanwhile, high resolution ortho image such as UAV(Unmanned Aerial Vehicle) ortho image is getting a lot of attention because of its usability. But it is hard to classify some feature using high resolution image because of image pixel's complexity. In this study, UAV Ortho Image was classified using eCognition software for extraction of buildings. As a result, buildings were classified by object-based classification methods effectively. The object-based classification method is expected to be available for classification of various high resolution images.