ABSTRACT:Earthquake can pose earth-shattering health hazards to the natural slops and land infrastructures. One of the chief consequences of the earthquakes can be land sliding, which is instigated by durable shaking. In this research, an efficient procedure is proposed to assist the prediction of earthquake-originated slope displacements (EIDS). New hybrid SVM-CBBO strategy is implemented to predict the EIDS. For this purpose, first, chaos paradigm is combined with initialization of BBO to enhance the diversification and intensification capacity of the conventional BBO optimizer. Then, chaotic BBO is developed as the searching scheme to investigate the best values of SVR parameters. In this paper, it will be confirmed that how the new computing approach is effective in prediction of EIDS. The outcomes affirm that the SVR-BBO strategy with chaos can be employed effectively as a predicting tool for evaluating the EIDS.

Commission VI, WG VI/4 KEY WORDS: IndoorGML, OpenStreetMap, Navigation, OGC, VGI ABSTRACT Navigation has become an essential component of human life and a necessary component in many fields. Because of the increasing size and complexity of buildings, a unified data model for navigation analysis and exchange of information. IndoorGML describes an appropriate data model and XML schema of indoor spatial information that focuses on modelling indoor spaces. Collecting spatial data by professional and commercial providers often need to spend high cost and time, which is the major reason that VGI emerged. One of the most popular VGI projects is OpenStreetMap (OSM). In this paper, a new approach is proposed for the automatic generation of IndoorGML data core file from OSM data file. The output of this approach is the file of core data model that can be used alongside the navigation data model for navigation application of indoor space.

Abstract. Continuous progress in navigation, sensor-based, and GPS technologies have made smart devices essential to our daily lives and many location-based applications. However, the trajectory datasets generated by these applications require the management of large data volumes while preserving their main properties and semantics. One of the most popular methods for compressing trajectory data offline is the Douglas–Peucker (DP) algorithm, but its principles should be applied to a diverse range of contexts when considering real-time trajectory data. This paper introduces a Flexible Douglas-Peucker algorithm (FDP) that takes into account the data’s diversity, underlying properties, and semantics. The proposed framework is applied to the Geolife benchmark dataset with a series of different thresholds that reflects different contexts and constraints when performing a trajectory compression process. The results show that the proposed algorithm achieves a significant compression rate while preserving trajectory data points that have a semantic role concerning different modes of transportation.

A common way to store information of spatio-temporal moving objects is to display the path of the objects as the form of a three-dimensional trajectory using the geographic location and time. In recent years, extensive research has been done on the trajectories. These studies have focused mainly on geometric aspects of trajectories. However, semantic trajectory is a relatively new concept that has been developed with the purpose of effective semantic analysis on captured data. In semantic trajectory, which is a secondary display of geometric trajectory, the movement of object is described as series of stop-and-move. Production of semantic trajectory from the collected raw data is a process with several steps. Due to the huge amount of data, one of the important processes is reducing the number of points of trajectory with maintaining the required accuracy by using compression techniques. However, data reduction techniques commonly are based on linear simplification and are not able to protect stop and move of trajectories. In this paper, a data reduction technique is presented which is based on combination of two distance functions for approximation of semantic trajectory. The first distance function has used speed of points to calculate the approximation error of trajectories. The second function is based on the development of well-known Douglas-Peuker algorithm, which assumes constant acceleration to calculate the approximation error. The proposed algorithm is implemented on real trajectory data and the results show improved performance compared with other algorithms in preservation of the stop and move of trajectories.

Navigation has been an inseparable part of human life especially in modern days, when the structures of cities and their buildings' indoor environments have been more complex. More than 80% of routine life of a typical citizen is spent in indoor and the indoor environment are getting highly complex due to the increase in sizes of the buildings. An important factor to a successful indoor navigation is the precise suitable map for the inside of the buildings. Collection and generation of indoor geospatial data is very time consuming and costly for a building. Using the concept of volunteered geospatial information might be a suitable solution to deal with this problem. This chapter addresses the extraction of a data model for indoor navigation from VGI. An efficient methodology is proposed and evaluated to extract the navigation data model from OpenStreetMap automatically to use in indoor navigation applications.