ENGINEERING JOURNAL OF GEOSPATIAL INFORMATION TECHNOLOGY
Year:2016 | Volume:4 | Issue:2|Papers Count:8

Ambient intelligent services are a set of services that are inside a specific zone provide seriveces for the users inside that area. Streets are one of the most important ambient intelligence environments in the cities. A smart street refers to a street which is able to provide its users relevant and proper services according to their conditions. Information services are one type of ambient intelligent services. Commercial messages of shopping centers, traffic statistics of various urban districts and police announcements from mobile or static urban stations are all important examples of urban information services. Since most service users, such as vehicle drivers, are constrained to move only on a network, service sites should be defined on the network as well. Determining the service provision region especially on the streets network and particularly for vehicles is an important issue that has not yet been studied in ambient intelligence. Utilizing the Network Voronoi Diagram (NVD) is one of the spatial analyses in Location Based Services, especially in the streets network; since adjacency to service centers should be considered in regard with the distance on the network. In the present study, a method for providing vehicles on the streets network with information services has been proposed. In this method, factors such as the adjacency of vehicles to services sites, servicing time and vehicle movement quality direction are taken into account. The obtained results from implementing the proposed method in one district of Tehran indicate 68 percent satisfaction with the system performance in utilizing network spatial analyses.

In this study, an algorithm for determination of hydrological and oceanic effects on gravity variations is introduced. Also, these effects on gravity variations for the case of Maule earthquake is being surveyed. For this purpose, data set of GLDAS hydrological model, JASON1 altimetry and World Ocean Atlas (WOA) is applied. With the help of hydrological model, the hydrological effects in land are computed and with combination of data of altimetry satellite and WOA, the oceanic effects in the ocean are calculated. This data is in the form of equivalent water layer thickness which is converted to mass changes. Then, this data is transformed to spherical harmonic coefficients using spherical harmonic analysis which is being used to compare gravimetric satellite observations such as GRACE. The hydrological and oceanic effects on gravity variations for the case of Maule, have maximum and minimum value about 2 and -0.5 microGal in radial direction, about 2 and -2 microGal in northward direction and about 0.6 and -0.6 microGal in eastward direction. Moreover, variations of these effects due to gravity variations in this region are 4 microGal which is a remarkable value for the aforementioned earthquake and is necessary for detecting tectonic signal.

By developing wireless mobile sensors, volunteered geographic information production and social cooperating increase, urban monitoring witnessed a considerable change. On the other hand, air pollution become one of the most important environmental challenge of Tehran and tackling it is not possible unless knowing the pollutants and their sources. Accurate investigation of spatial distribution of pollutants in cities requires development of spatial variability models of pollutants. But, high cost of developing and keeping of Air Quality Monitoring Network (AQMN) is one of the biggest problems in front of councils in developing such monitoring stations in cities. In this research, in addition to designing and building a mobile carbon monoxide monitoring system and calibrating it in the lab, data of pollutants concentration in 4 time periods in district 6 of Tehran was collected. Then by using land use regression method the spatial distribution model of this pollutant for the investigated area in each time period was determined. To reach this achievement, different environmental factors such as land use, Roads, elevation and traffic as independent factors and the measured pollutant concentration as dependent factor were applied to the mentioned model. Finally, this research led to provide carbon monoxide pollution maps for the investigated area, which are useful in finding high risk locations. Evaluating the statistical parameters show that in estimating pollutant concentration of different urban districts, the suggested method is practical to some extends.

High-resolution satellite images are extensively used in different fields. Geo-referencing process, as innate part of extraction of topographic terrains through these images, has been studied in many researches. In geo-referencing of satellite images, different models can be used, but rational functions are the most suitable options. Determining co-efficiency of rational functions is ill-condition problem, so to solve this problem Tikhonov regularization method has been used. In such regularization method, regularization selection parameter is very important. In present study, this parameter was calculated through two methods including: minimizing root mean square of errors (RMSE) and the L-curve for determining co-efficiency of rational functions. Then these two methods have been used in least standard squares of parametric model. Also combined model has been used to determine co-efficiency of rational functions in geo-referencing process. These calculations have been done for two different control-points groups with various numbers and accuracies. Using these two models (parametric and combined), regularization parameter has been calculated through L-curve and root mean square of error methods by 55 points. The results show that the root mean square errors and L-curve methods in parametric model led to accuracy of 4.45 and 5.40 pixels, respectively. Also in the combined model, root mean square errors and L-curve methods showed accuracy of 3.42 and 5.10 pixels, respectively. Above calculations were repeated with 120 points. This time, results show approximately same accuracies for both root mean square errors and L-curve methods.

Traffic elements are considered as important elemnts in traffic management. In Iran, overcrowding of automobiles in the cities led the traffic elemnts to be more vehicle-driven rather than pedestrian-oriented hence safety and well-being of pedestrains took less into consideration. Pedestrian bridge is one of the most important traffic elements which is strongly effects the security of pedestrians passing across the streets. Finding the best locations to install pedestrian bridges is normally done based on less comprehend and analytic prepared regulations. This causes low efficiency of many bridges and people choose the street itself instead of these safe passages. In this research alongside usage of spatial processing tools, an agent-based model is developed for assessing the pedestrians behaviors while going across the street. Agent-based model provides the possibility of taking the decision making process and its efficient situations into account which results in more reliable consequences. The developed model was used to evaluate four existent pedestrian bridges in Pasdaran Blvd, Shiraz. The results showed that performance of the least used bridge can be improved up to 27% by a 150 meters displacement. Then, the model was deployed to find two best places for a new bridge. The model anticipate that if a new bridge is constructed in each of new suggested locations, the pedestrians will use it more often than the existing four pedestrian bridges.

In the present research, the effectiveness of classification algorithms on coral reef habitat mapping was evaluated using Landsat-8 images acquired in 2013. For this purpose, Except Maximum Likelihood algorithm that is common method in coral reef habitat mapping, the efficiency of Neural Network and Support Vector Machine were estimated, as well. Along with data collected from diving in Lizard Island, eastern Australia, the research was accomplished by generalizability of the results to coral reef of Queshm and Larak Islands, Persian Gulf. For this, parallel to getting satellite images, through diving operations, field data were gathered from Queshm and Larak Islands. Depends on the number of classes, the results are varied, so that, Maximum Likelihood has the best efficiency in 2-class classification. However, increasing the number of classes shows more efficiency for Support Vector Machine and Neural Network. In 4-class classification, Support Vector Machine and Neural Network, improve the classification accuracy by 7% and 9% respectively. Implementation of methods in Queshm and Larak Islands shows the generalizability of Support Vector Machine results in this region by 8% improvement in comparison to ML and overall accuracy about 68%, whilst ANN shows the worst results in this region with overall accuracy of 58%, which is because of sensitivity of this algorithm to the number of training data. The capability of SVM to handle mixed pixels and training data deficit issues, cause it to be the best classifier in this case. Finally, because of appropriate performance in both regions and more robustness of results, the SVM by using medium resolution satellite images is selected as the optimized algorithm for mapping of coral reef habitats.

Positioning in road network environment requires a process with which can be able to match the raw coordinates, obtained from the positioning sensor (s), to the links of the network. Such matching process is necessary for two obvious reasons. First, positional data is not definite, and second, map coordinates are not absolute. Hence, there is a need for a process, known as map-matching, to reconcile two groups of coordinates. Accordingly, to provide some location-based services in network areas, performing a map-matching process seems inevitable. In this paper, after discussing several types of geometric map-matching methods, which is the most basic form of map-matching, a weighted-base map-matching algorithm is developed. The participating parameters’ weights are optimized experimentally. The algorithm takes three parameters as input: ‘Distance’, ‘Heading Convergency’, and ‘Relational Position’. Four types of modelling for ‘Relational Position’ are presented. The most effective type is then recognized after executing tests on algorithm performance. Also, comparing the performance of the suggested map-matching algorithm to the map-matching algorithms of the same complexity developed in other studies shows that the suggested algorithm is more efficient. This paper's suggested algorithm provided 95.5 percent of true link selection during the performance assessment.

Difference between geometrical data (dimensions and area) of parcels in cadastral title and cadastral map are motive for geometrical map distortions after geo-referencing. These distortions occur due to traditional surveying methods or environmental effects in maintenance of maps. Because of legal and financial complexities of cadastral maps compared with other map types, conflict between map and title makes social and legal problems. Therefore objective of this paper is recommend a method to decrease distortions of cadastral map through matching geometrical data of parcels in cadastral map with cadastral documents. -Recommended solution for this objective is using finite element transformation method. In this method control points constraint parcels dimensions and area constraint simultaneously convenient weights are added to continuity constraint and solved as a parametric listsquare. This process is implemented for two hardcopy maps and their title data then residuals decreased to a very low level. tests shows that RMSE of dimension improved from 65 cm to 1.2 cm, RMSE of area improved from 8.10 square meter to 0.0003 square meter and control points RMSE reduced from 1.12 meter to 0.65 meter.