Mina Karimi; Abolghasem Sadeghi Niaraki; Ali Hosseininaveh Ahmadabdian
Abstract
Extended Abstract Introduction Underground infrastructure such as electricity, gas, telecommunications, water and sewage are managed by different organizations. Since most projects in these organizations require drilling,and imprecise excavations will endanger infrastructure and result in extensive financial ...
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Extended Abstract Introduction Underground infrastructure such as electricity, gas, telecommunications, water and sewage are managed by different organizations. Since most projects in these organizations require drilling,and imprecise excavations will endanger infrastructure and result in extensive financial and physical losses, drilling projects require having accurate information about the infrastructure status. However, reaching accurate position of facilities such as pipes and cables is difficult due to their being concealed underground.Nowadays, ubiquitous computing and new developments in Geospatial Information Systems (GIS) can be an appropriate solution to such problems. This new generation of GIS is called the Ubiquitous Geospatial Information System (UBGIS). New technologies such as Augmented Reality (AR) can visualize this infrastructure on platforms like smart phones or tablets. Such technologies show spatial and descriptive attributes of these utilities more interactively, and thus can be applied as a modern solution for this problem. One of the major features of AR is identifying and locating real-world objects with respect to the person’s head or a camera. To have an accurate Augmented Reality, the position and orientation (pose) of the camera should be estimated with high accuracy. Therefore, exterior orientation parameters of the camera are required for AR and tracking. Different methods are used to calculate these exterior orientation parameters. One of the most common methods applies different sensors,such as Global Positioning System (GPS) and Inertial Measuring Unit (IMU),embedded in smart phones or tablets to calculate these parameters. These sensors include accelerometers, gyroscopes, magnetic sensors and compasses. Althoughsimple and fast, this method is not suitable for accurate cases, because sensors of mobile phones or tabletscannot provide such high accuracy. Vision-based (sometimes called image-based) method is another way of estimating exterior orientation parameters. In this method, fixed or dynamic images are used to determine the position and orientation of camera. The method is more complex and slower, but more accurate than the first one. Materials and Methods Regarding previously mentioned issues, the present article aims to visualize underground infrastructure using both sensor-based and vision-based approaches of Augmented Reality. Since the sensors embedded in a mobile phone or tablet do not provide such an accuracy (an accuracy of a few centimeters considering diameter of pipes and width of streets and pavements), a novel vision-based approach is proposed. In this method, image-based techniques and special kinds of targets, known as coded targets, are used to estimate camera’s position and orientation along with space resection method. In photogrammetry,space resection involves determining the spatial position and orientation of an image based on thesize of ground control points appearing on the image. Since space resection is a nonlinear problem, existing methods involve linearization of the collinearity condition and the use of an iterative process to determine the final solution using the least squares method. The process also requires determination of the initial approximate values of the unknown parameters, some of which must be estimated using another least squares solution. In order to obtain suitable initial values for space resection procedure, data received from GPS, accelerometers, and magnetic sensors are used and a low-pass filter is applied to reduce noise and increase precision. Then, due to improved camera pose parameters, the resulting virtual model is overlaid at its correct real worldplanimetriclocation. The planimetric coordinates are shown graphically on the ground and the Z coordinate (depth) is presented as a descriptive parameter. Results and Discussion Both proposed methods were implemented and tested in an Android Operating System. Camera pose parameters were estimated and the virtual modelwas overlaid at its correct real world planimetric location and shown on camera. Then, the results were compared and evaluatedusingthe well-known photogrammetry software, Agisoft, with the aim of modelling and precise measuring based on basic photogrammetry and machine vision. For sensor-based method, mean accuracy of the position parameters equals 4.2908±3.951 meters and mean accuracy of orientation parameters equals 6.1796±1.478 degrees,whilein vision-based method,these decreases to 0.1227±0.325 meters and 2.2017±0.536 degrees, respectively. Thus, results indicate that the proposed methodimprove accuracy and efficiency of AR technologies. Conclusion Augmented Reality is a technology that can be used to visualize underground facilities. Although,processing in sensor-based methods is sufficiently fast and simple, they lack the precision required for this purpose. Despite the fact that noise elimination and sensor integration using Kalman filter improves accuracy to some degree, it still does not reach the required accuracy. The present article sought to improve the accuracy of augmented reality in underground infrastructureusing targets. Results indicated that the machine vision and vision-based methods improve the accuracy. In drillings, third dimension (accuracy of height measurements) is as crucial as other parameters, thusit is suggested that future researches consider this not as a descriptive parameter, but as a three dimensional parameter to reach 3dimensional visualization.
Mehrdad Bijandi; Ali Asghar Alesheikh; Abolghasem Sadeghi Niaraki
Abstract
Extended Abstract
Introduction
One of the most important challenges of this era is the rapid growth of urbanization. According to the United Nations report, around 66% of the world’s population, equivalent to 6.4 billion will live in urban areas by the year 2050, while this number was only about ...
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Extended Abstract
Introduction
One of the most important challenges of this era is the rapid growth of urbanization. According to the United Nations report, around 66% of the world’s population, equivalent to 6.4 billion will live in urban areas by the year 2050, while this number was only about 746 million people, equivalent to 30% of the world’s population in 1950. This increasing trend is followed by the issues such as providing citizens with proper housing, providing energy, health services, education, employment, transportation etc., which should be resolved by appropriate policies and solutions. In this regard, modeling urban growth might play a key role in guiding urban strategies. Planners have traditionally used various models to formulate urban growth. However, these models lack sufficient dynamism and mobility and do not take the social behaviors of individuals and their interactions into account. The shortcomings of the previous methods have led to an increase in the use of modern modeling methods like cellular automata and agent-based model, particularly when presenting a perspective of the future urban growth is expected. Although the use of agents is a common tool in the modeling of the earth systems, few studies have been carried out in this regard in Iran, and various existing foreign studies are not in full agreement with the existing situation yet, due to the complex nature of the land use change problem. Therefore, researchers are still trying to provide new models by focusing on available findings and different aspects of the problem. In this research, a multifunctional system has been developed for the simulation of the urban growth by integrating the irregular cellular automata and the agent.
Materials and Method
The study area in this research is NajiAbad in the city of Kashan that is one of the city’s new districts. The texture of this district has taken shape in a designed and regular manner. The average area of its parcels is 250 square meters whose formation is mainly towards the northwest-southeast direction. Land use map of the year 2006 (1385), slope map, soil type, access map, floodway and river map were used in this research. The data of the year 1385 are used for the simulation of urban growth in 1392 and the results are compared with real data of 1392 and thus the results resulted from the model are evaluated. We have presented forecast of urban growth for the year 1400 subsequently. In the presented model, cadastral polygons act as irregular automata which have their own status and properties. In this model, the changes are updated in each replication, and each time step is considered to be one year. The first step in the evaluation model is the overall proportion of the land parcels which is one of the effective parameters in the decision-making process of the agents.
The calculation of the spatial proportion of the parcels for development is carried out by irregular cellular automata and based on four criteria of neighborhood, physical proportion, accessibility and constraints. Twelve effective factors were classified in proportion with these criteria and were normalized before the combination. The overall proportion of each land parcel has been calculated based on weighted linear combination function. In the next steps, the activity of the agents starts in the model. Many actors play roles in the development of the urban environment.
In this research, the agents are classified into 3 general classes of urban planning agent, developing agents and family agents. The family agents were classified into 3 classes of families with high, medium and low income according to the income level of the families. The urban planning agent estimates land demands and issues the segmentation permits for a number of lands. The developing agent calculates the profitability of the parcels, and segments those having separation permits and high profitability. The family agents search the environment and choose suitable land for habitation based on their preferences. This process is followed up until all family agents are settled and the demands are achieved.
Results and Evaluation
In this research, the output of the model and urban growth map in the study area for the year 2013 (1392) is calculated based on the input data of 2006 (1385). In this research, each time step is considered to be one year. In order to evaluate the model results, real data of 1392 has been used. In this research, the error matrix was used to calculate the accuracy of the results and comparison criterion is Kappa index. The Kappa index is a value between 0 (nonconformance of the calculated and observed maps) and 1 (full match of calculated and observed maps). Although there is no global standard, the Kappa index greater than 0.80 is often considered as a criterion of the proper conformation of calculated and observed maps. The Kappa index in this research was calculated to be 71% based on the error matrix. In this calculation the area of the previous developed regions has been eliminated. Although the elimination of the area related to these regions relatively reduces the overall accuracy of the model, it leads to a more accurate evaluation of the results. The accuracies of the user and producer in the developed lands feature a higher overall accuracy of the model, which can be a reason for desirable design of the model and its adaptation to the reality.
Conclusion
In this research, cellular automata were used to simulate the variations in the status of each land parcel in comparison with different spatial factors. Although the conventional cellular environment in cellular automata methods facilitates the possibility of urban growth modeling, it was attempted in this research to conduct modeling on the scale of irregular polygons of lands and in the form of base parcel. Although the use of cellular automata on this scale makes calculations more complicated and difficult, the results of modeling can be evaluated more realistically. In this research, the agents were classified into 3 general classes of urban planning agent, developing agent and family agents. The family agents were classified into 3 classes of families with high, medium and low income according to the income level of the families. The evaluation of the results with real data showed that the accuracy of the model was 71%. This study has been conducted in a vector structure and local scale that can be extended to other areas. This modeling can be done on a regional scale in future works.
Abolghasem Sadeghi Niaraki; Mahmoud Reza Delavar; Somaiieh Rokhsari Talemi
Abstract
Nowadays Smart traffic sensor network is considered as one of the newest ways of data acquisition in traffic management which with the possibility of intelligent monitoring of urban roads, leads to road accident reduction. Despite the importance of installing and deployment of such equipment, the most ...
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Nowadays Smart traffic sensor network is considered as one of the newest ways of data acquisition in traffic management which with the possibility of intelligent monitoring of urban roads, leads to road accident reduction. Despite the importance of installing and deployment of such equipment, the most important concern is to determine the optimal location for their installations. Therefore, what we are aiming at in this research, is to provide a suitable method to optimize the location of traffic sensors. The proposed method is a combination of FUZZY AHP and TOPSIS method. It should be noted that, in order to test the proposed method in this study, part of the urban road network in North America was selected as sample data. In the next step, according to traffic experts, the criteria for determining the optimal location were selected which included average annual traffic, crash severity, average slope and the distance of each connection in the urban network to places requiring traffic control. The FUZZY hierarchy method was used to determine the significance of input criteria. This method using FUZZY numbers in a pairwise comparison of criteria to calculate their weights, leads to an increase in the accuracy of computations. In the next stage, the weights calculated using TOPSIS method were used to rank the urban connections in the study area.
Eventually, after applying the above analysis using the score obtained from TOPSIS method, urban connections in the study area were classified in 3 different categories. Urban connections in the 1st category were selected as the ones with the highest priority for the installation of sensors. Therefore, these connections will be the top priority for the installation of traffic sensors.