نشریه مهندسی نقشه برداری و اطلاعات مکانی
سال دوم شماره 3 (پیاپی 7، شهریور 1390)

Support Vector Machine (SVM) has emerged in recent years as a popular approach for classification of remote sensing data. SVMs don’t require huge training samples and have little possibility of over fitting however; the accuracy of SVM mainly depends on the parameters selection of it. So, one of the significant research problems in SVM is the selection of optimal parameters that can increase the accuracy of this classifier. Regularization constant C and kernel function parameters exert a considerable influence on the accuracy of SVM. In recent years, the development of parameter optimization for SVM is supported by evolutionary algorithms and bio-inspired metaheuristic algorithms such as swarm-based methods. This paper evaluates the potential of one of the swarm-based bio-inspired optimization methods called Firefly Algorithm (FA) for SVM optimization. FA is a metaheuristic algorithm, inspired by the flashing behavior of fireflies. The primary purpose for a firefly's flash is to act as a signal system to attract other fireflies. Firefly algorithm is

Spatial Data Infrastructure (SDI) is one of the development tools in all political, social and economic aspects and its purpose is spatial data coordinating, facilitating management, and geospatial data sharing in a participating environment. As producing spatial data demand is increasing by time, and chairmen and high managers’ information about using spatial data for proper decision making is promoting, a serious need to establish national SDI is felt. Therefore, national SDI establishment provides a proper foundation for sustainable development of countries. Thus, we have surveyed the national SDIs (NSDI) situation and their components in 10 selected Asian countries and we‘ve compared them in order to find out implementation challenges and obstacles and to predict future issues and problems to represent successful NSDI implementation solutions. In this paper, the evaluation of the NSDI situation of these countries specified that Iran is in her elementary phases of NSDI implementation and have compiled its strategy plan by this time. Thus the recommended solutions in the result of this article would help to accomplish this national project effectively.

Building extraction becomes one of important fields of research in photogrammetry and machine vision. Involving airborne laser scanner (LiDAR) in geomatics, as an active sensor of 3D data acquisition, great evolution happened in data preparing to 3D outdoor object modeling. In consequence a great trend in developers and geomatics science researcher’s communities leaded them to develop techniques in extracting interesting objects from point cloud such as buildings. The LiDAR data don’t explicitly contain any geometric information and feature of objects. The feature such as planes, lines and corners can be only indirectly extracted by segmentation algorithms. In this research, a method for segmentation of LiDAR data for extracting buildings is proposed. In the proposed method, input data is an irregular LiDAR data and an adaptive clustering method for roof plane extraction from LiDAR data is implemented. So, to extract the roof structure, an assumption of planarity has been made. It is assumed that the roof can be modeled by a set of planar segments. In the proposed method the clustering is done without having any prior information about the number of the clusters. The clustering is done using FCM algorithm with considering maximum cluster number and then the extracted cluster will be modified using an iteratively split and merge technique. In this technique, small parallel planes are merged to create a new plane and a big plane created from different planes is divided to generate correct planes. This approach was evaluated using some buildings of used data set and the results prove high efficiency and reliability of this method for extraction of buildings.

Generalization is a prevalent concept in Cartography to which has been added new aspects such as model generalization with developments in GIS. Increasing demand for tailored and ubiquitous geospatial services like wayfinding, makes the context-aware generalization a noticeable research area in GIScience. Most of the wayfinding services use the network data model as the main spatial data model for their analyses. Whatever data or information that characterizes the situations relevant to users, systems and applications, can be considered as context. A context-aware service is as a service which can sense user, environment and device’s situations and respond to user requests concerning contexts to fulfill the user`s needs better. From the GIServices perspective, the context of a query could be the location of the device, environmental settings that query is made in, the time, the activity of the user, user`s personal information, the user`s favorites and information needs, the user`s cognitive map of environment, the mode of travel, the purpose of travel and the device’s technological specifications. In this paper, we propose and implement a method for context-aware network abstraction and generalization for presenting routes to the user. In this method, landmarks, POIs, personally meaningful places and current user location together play the seed role in the abstraction and generalization process. Network Voronoi Diagrams are used in this thesis for network partitioning with aforementioned places as generators. Edges and nodes which are in a voronoi diagram will be abstracted to a node in the next generalized level in the hierarchy. Repeating this procedure will produce other levels of hierarchy, based on selecting most salient generators in each level and use them as generators to form the next level. This hierarchical structure will be used for presenting routes.

The temporary housing problem plays essential role in disaster management since its usage would significantly reduce the number of casualties (human and financial tolls). Different factors such as minimizing the transportation of people and homogeneous distribution of population based on safe areas’ capacity should be simultaneously optimized in order to achieve the best solution for temporary housing problem. Two objective functions were defined and their weight was estimated using AHP, then a single objective function was produced using weighted average of the initial objective functions. The objective of this research is to assess the functionality of Bees algorithm in the process of optimizing the objective function. BCO is inspired by the natural foraging behavior of honey bees to find the optimal solution. In order to calibrate the aforementioned algorithm a batch of simulated data has been used and then in order to assess the functionality of the calibrated algorithm the real data of the 7th district of Tehran has been used. Then a repeatability test has been conducted in order to assess the accuracy of the algorithm. The results showed that by using this algorithm the assignment of population to the safe areas is facilitated.