JOURNAL OF GEOMATICS SCIENCE AND TECHNOLOGY
Year:2014 | Volume:3 | Issue:4|Papers Count:10

The ionosphere is the part of the atmosphere in which the number of free electrons is so high that it significantly affects the propagation of radio waves. Since the deactivation of SA, the accuracy of the differential positioning with GPS is mostly dominated by the refraction delay of the GPS carrier waves in Ionosphere. To improve the accuracy of real time positions, it is customary to use the existing models in order to account for various sources of errors, including the effect of Ionosphere. Application of the single layer model, in which the electron content of Ionosphere is assumed to be condensed on a thin shell at a specific height, is a well-established technique to account for the ionosphere delay. In contrary to this model, multi-layer modeling can provide three-dimensional information on the spatial distribution of the electrons in atmosphere. In this method, spherical harmonics and empirical orthogonal functions are the base functions in use for modeling the horizontal and the vertical content of the electron density. This is at the cost of using regularization techniques for solving the corresponding problem. Therefore, the developed model should be calibrated, i.e. the corresponding regularization parameter should be chosen, based on the a-priori information of the electron content. In this study, a 3D-modeling of the electron content has been constructed using the GPS measurements over Iran. The Ionosonde Data in the Tehran station (f=50.64, l=35.87) have been used for choosing an optimum value for the regularization parameter. To apply the method for constructing a 3D-image of the electron density, GPS measurements of the Iranian Permanent GPS Network (at 3-day in 2007) has been used. The instability of solution has been numerically analyzed and the Tikhonov method has been used for regularizing the solution. To come up with an optimum regularization parameter, the relative error in the Electron density profile computed from Ionosonde measurements and their 3D model are minimized. The modeling region is between 24 to 40 N and 44 to 64 W. The result of 3D-Model has been compared to that of the International Reference Ionosphere model 2007 (IRI-2007).The result shows that the electron density has 6×1011ele/cm3 diurnal variation and 9×1011ele/cm3 seasonal variations especially in winter months. This method could recover 66% to 99% of the ionosphere electron density.

In this study we will review potential and gravity gradient changes due to Sumatra’s 9.2-magnitude earthquake on 26th Dec 2004 using data collected from GRACE Satellite. The gravity gradients clearly show the variations of mass distributions in the ground. The previous studies have shown that filters and softening algorithms were used to elimination of stripe error of GRACE observations. In this study we find the application of gravity gradient in removing stripe error of observation of GRACE satellite and we see that with these gradients in direction, gradient tensor components such as and cause eliminate GRACE observations strip errors however with these gradients in direction, gradient tensor components such as and would cause fluctuations to intensify in north south direction. Also we find the maximum and minimum value of gravity gradient changes (1.5mE and -1.2mE) will be earned in direction.

There are several sources of systematic errors which affect the precise leveling observations and cause limitations to obtain high quality measurements. Some of these sources are refraction effects, rod scale error, vertical movement of instrument, temperature effect on rods and level, non-vertically of rods, bench mark movements. So far, many efforts have been carried out by different people to eliminate or reduce significantly the effects of systematic errors in leveling observations. These investigations mainly focus, especially in Iran, on physical modeling of atmospheric refraction and leveling rod expansion during the measurements. Based on the developed models of systematic errors, National Cartographic Center of Iran (NCC) uses the relative thermometer sensors along the precise leveling rods simultaneous with height difference observations to better model atmospheric refraction as the most important source of the systematic errors. In fact the relative thermometer sensors could be a great help to determine the vertical temperature gradient along the leveling rods. Obviously these auxiliary data imply more expenses to the NCC. In spite of all these tries, the precise leveling observations are not yet free of systematic errors due to atmospheric refraction, rod scale error and other unknown resources of errors. In this research, we try to run a new and simple method based on mathematical modeling of systematic errors resultant. The basic of this method is the expectation of errors must be zero, as a fundamental condition before adjusting the observations. In absence of any systematic error in precise leveling observations, we can see the forward-backward discrepancies along a leveling line vary around zero as it is expected. But in reality we do not have such a case and see an important accumulated height differences when we go far from the initial leveling bench mark in leveling lines. In fact we confront a trend analysis problem and should try to estimate a mathematical model to the trend by using approximation theory. Supposing the best fitted mathematical model is representative of the systematic errors resultant for each leveling line, we remove it from the corresponding height difference observations as a mathematical correction to the measurements. After obtaining the corrected observations, based on the mathematical model, we calculated a new series of height differences as corrected observations and put them in adjustment process. Finally we did a comparison between the results from this investigation (mathematical method) and the results from the current method (physical model). The results of the comparison show that there is not a significant difference between raw heights (without any corrections) and physical corrected heights (after physical corrections). This means the refraction model used in NCC is not efficient and it should to find a better model to remove systematic errors in leveling observations. The results as well as show the mathematical model is an alternative way to reduce systematic errors while there is no any available physical data.

Rapid expansion of web data containing spatial information requires new approaches which are capable of huge data handling. Semantic web paradigm offers appealing solutions for heterogeneous and sporadic data sets by setting new and different definitions of Data Base Management Systems (DBMS). On the other hand, due to various understanding of place among different individuals, there are many criteria for cognition of place and place categories, so these different criteria lead to more distinctive annotations (so-called affordance) for Points Of Interest (POI) modeling. The design of specific schemas for these types of POI’s affordances is heinous in relational DBMS while spatial semantic modeling through graph DBMS expedites deployment of affordance-based storage and retrieval frameworks. In this study, available specifications and technologies related to spatial semantic functionalities are addressed and the current state of spatial semantic data formatting and interpretation is presented. The designed information system utilizes semantic web specifications to store and retrieve affordance-based POI’s information and a sample data set is used for applicability of the system. In order to evaluate achieved results, retrieved data by spatial semantic queries are compared to similar information in relational data bases, therefore spatial data loading in GDBMSs and RDBMSs are compared and results are analyzed.

Nowadays, unmanned aerial vehicles (UAV), as the mobile multi-sensor platforms, are a necessary instrument for immediately spatial information gathering after occurrence of natural hazards. UAVs have a substantial role in improvement of search and rescue missions during natural hazards with fast and systematic monitoring of dangerous areas those are not accessible to relief workers. On the other hand, UAV mission planning is a fundamental stage to achieve the autonomous navigation. In this domain, computing optimal trajectories considering mission requirements and environmental conditions is a required step in path planning of autonomous UAVs. In this paper, after analyzing the role of UAV in upgrading the search and rescue missions, an improved bacterial foraging algorithm is proposed and implemented for UAV path planning. For performance evaluations, the algorithm is compared with other robust methods. Finally, results of UAV search and rescue mission simulations and research outcomes are presented.

In recent years, in order to deal with excessive utilization of cars, the use of public bicycles has become as an important urban transport approach. To tackle with this, the place of bike stations and its relation with different travel demands are major keys. Geographic Information System (GIS), due to the ability to perform complex spatial analyses is used vastly in urban transportation planning. In this research, a GIS based multi-criteria decision analysis approach is utilized for spatial planning of public bicycle stations. The proximity to the road junctions and accessibility to public parking as well as distance from existent public bike stations were taken as evaluation criteria. To model uncertainty of expert opinions regarding some criteria, fuzzy logic model was used. The proposed method was tested in 7th region of Tehran, Iran. The results approved that the proposed method is robust to efficient spatial planning of public bicycle stations and determining the obstacles and capabilities of various areas in the case study in relation to the considered evaluation criteria. The proposed method can be used as a useful tool for nonmotorized urban transport planning.

In spite of huge advantages, Volunteered Geographic Information (VGI) still lacks a mechanism for quality control. As such, the quality of this kind of data is believed to be the main limitation in its use. Many scientists have worked on this issue so far, but the majority of the researches have focused on the internal and external quality of VGI. Pragmatic quality has usually been ignored, despite its significance. This paper proposes a method for assessing pragmatic quality of volunteered geographic information based on linguistic quantifiers and OWA operator. In this method, to assess the pragmatic quality, first internal and external quality of volunteered data should be assessed. In order to evaluate internal and external quality a few mid-level and low-level indicators are defined for each and by applying OWA on these indicators the value of quality is computed. As an example, this method is used to assess pragmatic quality of volunteered data gathered about the vandalism of historic monuments of Tehran, Iran. The results of this research showed that internal quality has a greater impact on pragmatic quality than external quality and that the expertise of the volunteer has a remarkable impact on the pragmatic quality of data he or she has produced.

In order to enhance the accuracy of the image clustering, many clustering algorithms such as semi-supervised clustering, clustering ensembles and Coclustering have so far been proposed where the most effective strategy is when spatial context in clustering is considered. In this study, four clustering methods in which the spatial neighborhood information is used are investigated. These include: using texture information, object based clustering, Markov random field clustering and applying majority filter on the clustering results. These four methods are tested on the two synthetic and two real high resolution satellite images. The results show that using texture information in clustering can enhance the overall accuracy of the clustering of the real images up to 10.8% relative to the simplek-means clustering. Markov random field clustering of the synthetic images shows 22.8% improvement in overall accuracy. The results show that using spatial context can enhance the overall accuracy of the satellite image clustering.

UAV aerial digital images have many applications in spatial information extraction, map revision and mapping. The purpose of geometric correction methods for evaluating research UAV aerial digital images include : rigorous models, the implementation of the whole process of photogrammetry aerial camera calibration step to triangulation and adjustment in several stages, the preparation of largescale ortho photo blueprints to, including the models to implement, the non-rigours models ( the two-dimensional polynomial transform, Affine, second and third degree, fraction functions, convert, primarily )and the use of artificial intelligence and computer modeling to determine the exact position of the side effects of using a map is provided. The best results of the implementation of the rigorous modeling in order for the Ms thaty x (0.2105m), y (0.2856m) and the best results of the nonrigorous modeling with multiple functions, including two-dimensional third- rate residuals of the control points 2 pixels (about 24 cm m), respectively.

Today, 3D models of urban as one of the most important information are needed in many engineering applications such as urban planning, virtual tourism, navigation and emergency response. In recent years, by introducing the LiDAR system and using the 3D geo-referenced data, different methods have been proposed for 3D building modeling. Due to direct accessibility of 3D coordinate of LiDAR points, planar patch’s mathematical equations of building roofs could be determined accurately. However, extracting the edges and boundary of buildings with high accuracy from LiDAR points is a difficult task and will not be always precise. In new aerial laser scanning systems, in addition to using laser to adopt 3D point’s cloud of surface features, equipped by high resolutions small format RGB camera. So besides spatial data of features, RGB and spectral information are also available. In this research a method based on fusion of LiDAR point cloud and aerial image data sources has been proposed. In this study, firstly by using 2D plane (building footprint), points located inside polygon of each building are extracted from the overall scatter, individually. In the next step, the mean shift clustering algorithm applied to the points of different buildings in the feature space. Finally the segmentation stage ended with the separation of parallel and coplanar segments. Then using the adjacency matrix, adjacent segments are intersected and inner vertices are determined. In the other space, the region of any building cropped in the image space and the mean shift algorithm applied to it. Then, the lines of roof’s outline edge extracted by the Hough transform algorithm and the points obtained from the intersection of these lines transformed to the ground space. Finally, by integration of structural points of intersected adjacent facets and the transformed points from image space, reconstruction performed. In order to evaluate the efficiency of proposed method, buildings with different shapes and different level of complexity selected and the results of the 3D model reconstruction evaluated. The results showed credible efficiency of method for different buildings.