Efficient path computation is essential for applications such as intelligent transportation systems (ITS) and network routing. In ITS navigation systems, many path requests can be submitted over the same, typically huge, transportation network within a small time window. While path precomputation (path view) would provide an efficient path query response, it raises three problems which must be addressed: 1) precomputed paths exceed the current computer main memory capacity for large networks; 2) disk-based solutions are too inefficient to meet the stringent requirements of these target applications; and 3) path views become too costly to update for large graphs (resulting in out-of-date query results). We propose a hierarchical encoded path view (HEPV) model that addresses all three problems. By hierarchically encoding partial paths, HEPV reduces the view encoding time, updating time and storage requirements beyond previously known path precomputation techniques, while significantly minimizing path retrieval time. We prove that paths retrieved over HEPV are optimal. We present complete solutions for all phases of the HEPV approach, including graph partitioning, hierarchy generation, path view encoding and updating, and path retrieval. In this paper, we also present an in-depth experimental evaluation of HEPV based on both synthetic and real GIS networks. Our results confirm that HEPV offers advantages over alternative path finding approaches in terms of performance and space efficiency.

In game Industry, the most trending research area is shortest path finding. There are many video games are present who are facing the problem of path finding and there is various algorithms are present to solve this problem. In this paper brief introduction is given in the most using algorithm for path finding and A* algorithm has been proved the best algorithm for resolving the problem of shortest path finding in games. It provides the optimal solution for path finding as compare to other search algorithm. At the start of the paper, brief introduction about the path finding is given. Then the reviews of different search algorithm are presented on the basis of path finding. After that information of A* algorithm and optimization techniques are described. In the last, application and examples how the path finding techniques are used in the game is addressed and future work and conclusion are drawn.

Road networks play a significant role in modern city management. It is necessary to continually extract current road structure, as it changes rapidly with the development of the city. Due to the success of semantic segmentation based on deep learning in the application of computer vision, extracting road networks from VHR (Very High Resolution) imagery becomes a method of updating geographic databases. The major shortcoming of deep learning methods for road networks extraction is that they need a massive amount of high quality pixel-wise training datasets, which is hard to obtain. Meanwhile, a large amount of different types of VGI (volunteer geographic information) data including road centerline has been accumulated in the past few decades. However, most road centerlines in VGI data lack precise width information and, therefore, cannot be directly applied to conventional supervised deep learning models. In this paper, we propose a novel weakly supervised method to extract road networks from VHR images using only the OSM (OpenStreetMap) road centerline as training data instead of high quality pixel-wise road width label. Large amounts of paired Google Earth images and OSM data are used to validate the approach. The results show that the proposed method can extract road networks from the VHR images both accurately and effectively without using pixel-wise road training data. scribble annotation for road extraction. The ground truth of the pixel-level annotation in Figure 1b should label every pixel, which is difficult to generate. Figure 1c represents the scribble annotation, which can be easily obtained from OSM. Because the full annotation dataset is expensive to obtain and the scribble annotation is easy to generate, the study of road networks extraction using scribble labels is of great importance.Recently, weakly supervised learning was popular in image segmentation [13][14][15][16]. In these methods, scribbles [17,18], bounding boxes [19,20], clicks [19], and image-level tags [18] are used as supervision for image segmentation. In this work, the OSM centerline is used as a typical scribbles supervision for road extraction.In order to improve annotation efficiency and road extraction performance for automated VHR (Very High Resolution, spatial resolution 0∼2 m/pixel) images interpretation, this paper proposes a weakly supervised method to extract the road network supervised only by the scribble annotation OSM centerline. In this method, graph cut theory and a deep learning technique named Multi-Dilated-ResUNet (MD-ResUNet) are used to make efficient roads extraction.The main contributions of this paper are as follows:1. A novel deep learning approach based on revised ResUNet with hybrid loss is proposed for road extraction, which can only be supervised by weakly labeled OSM centerline instead of carefully notated pixel-wise road width information. 2. In order to improve the performance of the proposed model furtherly, a novel multi-dilation network with learnable parameters is added to conventional ResUnet. Th...

Volunteered geographic information (VGI) projects, such as OpenStreetMap (OSM), provide an alternative way to produce geographic data. Research has proven that the resulting data in some areas are of decent quality, which guarantees their usability in various applications. Though these achievements are normally attributed to the huge heterogeneous community mainly consisting of amateurs, it is in fact a small percentage of major contributors who make nearly all contributions. In this paper, we investigate the contributing behaviors of these contributors to deduce whether they are actually professionals. Various indicators are used to depict the behaviors on three themes: practice, skill and motivation, aiming to identify solid evidence for expertise. Our case studies show that most major contributors in Germany, France and the United Kingdom are hardly amateurs, but are professionals instead. These contributors have rich experiences on geographical data editing, have a decent grasp of professional software and work on the project with enthusiasm and concentration. It is less unexpected that they can create geographic data of high quality.

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