نشریه علوم و فنون نقشه برداری
سال دهم شماره 4 (پیاپی 40، بهار 1400)

Today, volunteered geographic information (VGI) has provided an innovative, fast, and low-cost approach to data collection. Using this approach will lead to problems such as semantic heterogeneity. Ontology is one of the newest modeling scientific methods and that can solve the data semantic heterogeneity problems. But using ontology to collect information has challenges in volunteered geographic information systems. Since ontology is derived from the technical procedure so, for use it, specialized tools and specialized information are needed and its output will be used by experts. As people are not usually experts and do not have specialized tools, so in this research, we have proposed the method for utilizing ontology in VGI systems to prevent the creation of semantic heterogeneity in the data. Besides, volunteers don’t need to train or to have specialized tools for using this system. In this method, we are gathering information and developing the ontology model of information and finally an understandable conceptual model for the public at three levels of details using the classes, subclasses, relationships, and features in the ontology model will be built. Then we are relating this conceptual model to the ontological model using a look-up table. By doing this, the volunteers are entering their information in the VGI system based on the conceptual model, and the result of the ontology model will be provided to the decision makers as output. Finally, we selected the surface water resources as the case study and then evaluated the conceptual model from viewpoint of the simplicity and ambiguity of the volunteers and the lack of need for specialized information to use it. According to the results, approximately 83% of the concept model is unambiguous and understandable to volunteer without training. Also the results of the ontological model were evaluated in an efficiency viewpoint for decision making by experts. The results of this evaluation also are showing 88% of the output information from the ontology model is appropriate for decision making. Therefore, the results of the evaluating are showing the effectiveness of the conceptual and ontological model for the intended purpose.

Considering that earthquakes are one of the most common natural crises in the world, especially in Iran, so far, a lot of research has been done to evaluate the vulnerability of buildings to earthquakes. The main problem in the face of an earthquake is the complete unpreparedness to deal with it, that one of the basic strategies to create this preparedness is to evaluate and be aware of their resistance to earthquakes in different risk scenarios and conditions. In this study, using the opinions of geologists, structures and earthquakes engineers, urban planning experts, passive defense and architecture engineers, 8 vulnerability criteria extracted and fuzzy standard criterion maps produced according to each of them. The Fuzzy AHP technique was also used to weigh the criteria and the fuzzy Simple Additive Weighting operator was used to combine the standard maps. To defuzzify fuzzy vulnerability maps and generate vulnerability maps in different risk conditions, the Ordered Weighted Averaging operator (OWA) was used. Analysis of the results showed that in the most optimistic scenario, 39% of the buildings have low vulnerability and in the most pessimistic scenario, 49% of the buildings have high vulnerability. Sensitivity analysis technique was used to evaluate the model, which showed that the results are highly reliable.

One of the important issues in urban management is locating of urban parks. Urban parks, as one of the most important public service spaces in the city, have a great role in promoting the social, cultural, economic and environmental conditions of urban areas. Optimization can be considered as an effective tool for this problem. The key factors to achieve a successful public park are accessibility and appropriate link between different features of urban structures. Therefore, it is necessary to identify effective criteria and appropriate tools in urban park site locating. The purpose of this paper is optimal locating of urban parks sites in the city of Samawah (Iraq) using Geo-spatial information system and genetic algorithms. To this end, objective functions include; minimize population movement distances and homogeneous distribution of people in proportion to the capacity of parks. Based on the proposed method and considered criteria; Land use, distance from urban transportation network, distance from rivers, population density and distance from noise (factories, etc.), four suitable areas for the construction of a park in the city of Samawah were determined.Scrutinizing of the characteristics of the determined sites for the construction of the urban parks, the high capability of the genetic algorithm in this application was proved. The northern part of the river has high population density. The genetic algorithm has chosen a location between the eastern and western blocks from the all candidates to cover the population demand. In the southern part of the river; three locations have been proposed for the construction of urban parks. The first and most important location is located in the central and high densely populated part of the city, where the existing barren land has provided ideal conditions for the construction of urban park. This location is the best option for building a park due to its proximity to various land use, proximity to building blocks and the densely populated area, as well as ideal access to the transportation network.  Third location; it includes the barren land covers to the west of the citychr(chr('39')39chr('39'))s residential area. Due to its proximity to the river and urban blocks, this location is a suitable option for the construction of public parks. Fourth case; it is located in the southern part of the region. This location is one of the best locations for park construction due to its proximity to different land uses. The proposed method and results could provide valuable information to managers when locating urban parks in Samawah City.

Interpolation is the process of estimating unknown values that are located between known values. Usually this process is done using different kinds of continuous functions. One of the most common types of continuous functions which can be used for interpolation, are polynomials. In approximation theory polynomial interpolation is utilized to approximating a complex function using a polynomial. In this issue polynomial coefficients can be determined using different computing methods. The basic procedure to determining the coefficients, is solution of vandermonde system. The system however, has only theoretical significance, since its solution by numerical methods is ill-advised on all counts (computational effort, storage requirement, accuracy). This is the reason to using some alternative methods such as Newton and Lagrange. These methods are two well-known representations of the unique interpolation polynomial. Newton representation is based on determining divided differences, while the other one is a very elegant alternative representation of Newtons general formula that does not require the computation of finite or divided differences. Lagrange representation can be utilized for any sets of interpolating points. In some cases Lagrange representation is used for interpolating between equidistant nodes. For example in GNSS positioning it is a common issue to find the satellites position using the coordinates are given in 15 minutes constant interval. generally computing Lagrange basis polynomial using current method requires O(n2) operations. So when we use polynomials with high degrees for interpolation, we expect a significant increase of computational effort. According to this issue in the last article we introduced a recursive algorithm to obtaining Lagrange coefficients using equidistant nodes. By the use of this algorithm, we had a significant improvement in computations speed. Despite the usage of equidistant interpolation, it is not a good idea to use evenly spaced points to approximating a function. Because in such a situation interpolated polynomial has wild oscillations near the edges of interpolation interval and does not converge to the main function, specially in high order polynomials. This nonconvergence is called Runge phenomenon. To avoiding this problem, other sets of interpolating points should be used, with more density at the end points of interval. The simplest examples of such a point sets, are the families of Chebyshev points. These points are set of zeros of the Chebyshev polynomial. By using Chebyshev nodes, interpolation will be more accurate. Since unwanted oscillations are gone. Due to the mentioned advantages of Chebyshev nodes, in this paper we are going to introduce a recursive algorithm  to obtaining Lagrange coefficients using these sets of points. computing Lagrange basis polynomial using this method requires O(n) operations unlike the current method. So by the use of recursive algorithm, we expect speed increase in computations process. To investigating this issue we obtained Lagrange basis polynomial for all integer numbers within [1,1000] interval. All of coefficients were computed for different polynomial degrees from 1 to 10 using MATLAB. In the following we recorded calculating times for both of computing algorithms and also for different polynomial degrees. After checking computing times we found a significant increase in processing speed by the use of recursive method. Although this method reduces processing time for all polynomial degrees, it is more effective when we use polynomials with high degrees. In other words when we use a polynomial with degree of one, the recursive algorithm is 1/3 times faster in comparison with usual algorithm; But when we use a polynomial in degree of 10, it is 3 times faster. So we conclude that it is logical to use this algorithm specially when we use high degree polynomials for interpolation.

One of the inseparable parts of human life in all parts of the world and time periods is the occurrence of various natural disasters such as floods, earthquakes, etc. Some countries, such as Iran, are more vulnerable to these events due to their geographical location and special climate conditions. Disaster management is important in the event of such disasters, which can reduce the loss of life, property, and mental health and prevent the imposition of exorbitant costs on the country. One of the most important aspects of disaster management is the proper and equitable distribution of the aid. The disadvantages of conventional and non-systemic approaches are lack of accurate information on the position of the affected areas and the type of needs of the people living there, inconsistency in the allocation of aid between the different organisations, and lack of awareness of the type and amount of aid allocated. Such a distribution causes problems, such as the unfair distribution of aid in the form of accumulation and loss of certain items in some areas, and the scarcity and high cost of certain items in others. A spatial decision-making system based on volunteered geographic information was proposed and implemented in this study in order to properly and fairly distribute aid in the event of natural disasters, which supports the disaster managers’ needs and functions. The proposed framework consists of two main phases. In the first phase three groups of users collect data on the affected areas and their needs in the context of volunteered geographic information.This data includes location data and the needs of the affected areas. After collecting a huge amount of data, in the second phase, with the help of multi-criteria decision analysis tools, the affected areas are prioritized in terms of need for assistance and items. Therefore management and monitoring of the process of distribution of items and allocation of aid to different affected areas is done. The advantages of using the proposed system are accelerating the identification of damaged areas, timely and targeted distribution of aid in a manner appropriate to the type and amount of needs, effective participation of injured and relieved people, prevention of loss of aid and possible abuses and saving time and money.

In this paper, first the method of solving the linear weighted total least squares, and then its generalization to nonlinear state is discussed; as the problem-solving model for determining the coordinates with pseudo-range GPS observations is fully consistent with this model. Available techniques for solving the TLS are based on the SVD and have a high computational burden. Furthermore, the other presented methods that do not use SVD, need large matrices, and there is need for placing zero in the covariance matrix of the design matrix, corresponding to the errorless columns, which increases the matrix size and, as a result, raises the volume of the calculations. But in the proposed method, problem-solving is done without need for SVD, without introducing Lagrange multipliers, and avoiding the error-free introducing of some columns of the design matrix by entering zero in the covariance matrix of the design matrix. It is performed only using easy equations and on the basis of summation principles, which results in less computing effort and high speed. In the following, an optimal method for weighting the design matrix is presented, which can yield a much better answer to the unknowns in the presence of many failures (here, up to three failures are assumed and tested). Besides, it can estimate the residuals vector so that the failure observations would have larger magnitudes than the others, and could help with detecting them in a safer and more feasible way with respect to any other method.

Today, in big cities and in the road network, traffic congestion and its management has become a major problem, which results in this traffic congestion in addition to the mental and physical problems it creates for citizens, nothing but increased fuel consumption, increased air pollution and waste. Gone is not time and energy. One of the emerging solutions for traffic and transportation management is the discussion of ride sharing, in which the driver shares the empty seats in his vehicle with people who share a travel and time plan with him, which in addition to reducing The cost and energy that follows for both the passenger and the driver will increase the efficiency of the transportation network and reduce the traffic and the resulting problems. One of the most important issues in shared passenger services is the discussion of meeting points. This is important because in shared passenger services, drivers are looking to reduce the distance traveled and fuel consumption to reach the passenger, from On the other hand, each passenger has a specific time window and many of them want to walk a distance to reach a safe and comfortable place. Since the variables of this research, which include: slope, speed of the passage, width of the passage, distance to the intersection, distance of the passenger and the amount of deviation from the driverchr  path, the variables are continuous, linguistic and inaccurate and is a continuous system. , The use of fuzzy logic is suggested. Therefore, in this study, by combining fuzzy logic and spatial information systems, a solution to identify and evaluate meeting points has been presented. In this study, the study area is the metropolis of Tehran. The results of this study indicate that with increasing the number of available meeting points, the number of suitable meeting points and adaptation rate increases by 15% and also the rate of deviation from the main route for the driver is approximately 14% and the distance traveled by the passenger to reach the point. Meetings are reduced by up to 40%.

Dams as important man-made structures need to be monitored continuously and precisely. Variations in water surface area play an important role in this major. SAR images of the Sentinel-1 mission have been considered a promising tool to monitor water dynamics due to their cloud-proof, illumination-independent, and high spatiotemporal resolution properties. In this study, to monitor the surface area of the Doroudzan dam reservoir, SAR images of Sentinel-1A mission in two polarization (VV and VH) in 2018 and 2019 are used. After image pre-processing, images are classified into two-class, i.e. water and non-water, based on the thresholding method, and corresponding threshold values are selected from the image. Next, the area of the water body was computed. Then, the time series of the surface area of the dam reservoir is obtained from VV and VH polarizations and was compared with the time series obtained from in-situ areas computation of the Doroudzan dam reservoir. The results showed that: (1) The threshold values for the classification of images and water separation from non-water for VH from -21.36 to -23.01 dB and VV from -13.47 to -19.08 dB. (2) VV polarization with relative RMSE of %5.83 and correlation coefficient of %97.55 compared to in-situ surface areas achieved higher accuracy as compared to VH polarization which resulted in relative RMSE value of %9.21 and correlation coefficient of %83.63 as compared to in-situ areas; VV is more sensitive to water cover than VH and is more stable under seasonal variations than VH. (3) The surface area of the Doroudzan dam reservoir area had started to increase in February 2018, and then declined in May 2018; It also started to rise again in December 2018 and then declined in May 2019. The surface area obtained from VV polarization of the Doroudzan dam reservoir was at its maximum on 20 April 2019 with 43.6187 km2 and a minimum on 28 September 2018 with 24.2241 km2.

Point of interest (POI) is one of the important applications of location-based social networks (LBSNs) for users and business managers. LBSNs include various complex relations (i.e., POI-POI, user-user, user-POI, and so on), that more accurate modeling of them can lead to making a better recommendation. Since some relations are much more sophisticated than pairwise relations, and thus cannot be simply modeled by a graph. This study proposes a model for calculating the similarity of POIs and users based on hypergraph structure and by integrating that into the collaborative filtering (CF) method it can improve the recommendation performance. The results obtained from the real data set, Foursquare, show that the proposed model performs better than state-of-the-art methods in terms of accuracy. Taking high-order relations between POIs and users into account can improve recommendation performance by 2.7% in terms of accuracy. By integrating the proposed similarity learning into the collaborative filtering (CF) method, our method obtained approximately 33% improvements in accuracy compared to the traditional similarity learning methods.

Global Digital Elevation Models (GDEMs) are one of the most important sources of elevation data. In recent years, GDEMs have become increasingly popular with researchers due to their global coverage and free accessibility. The most commonly used GDEMs are AW3D, ASTER, and SRTM. Each of these models is produced by different technologies and have different strengths and weaknesses. This issue indicates that these data are not necessarily consistent with another, and their accuracy is dependent on the local topography of the earth. The main objective of this research is to fuse global digital elevation models to produce a model with higher vertical accuracy. In this regard, in this study, a two-step approach is proposed for fusing GDEMs. In the first step, a Geographically Weighted Regression (GWR) model is used to determine areas of the Earthchr(chr('39')39chr('39'))s surface that have similar properties. In other words, using the GWR model, regions of the study areas with similar behaviors are classified into the same classes. At this step, each of these study areas is classified into three, five, and seven classes. Among these modes, for both study areas, the best results are for five Class mode. In the second step, to fuse GDEMs, the optimum weight of each class defined for each of AW3D, ASTER, and SRTM models are estimated using the particle swarm optimization (PSO) algorithm. In order to evaluate the accuracy of the proposed method, it has been used to produce the fused DEM for two study areas of BumeHen and TazehAbad. In the first case study (BumeHen), the amount of Root Mean Square Error (RMSE) on test points in five class mode for AW3D, ASTER and SRTM are 4.58, 8.69 and  4.70 meters respectively, while it’s 3.97 meters for fused DEM. In the second case study (TazehAbad), the amount of RMSE on test points in five class mode for AW3D, ASTER, and SRTM are 3.33, 7.31, and 3.17 meter respectively and it’s 2.74 meters for fused DEM. The results show that the proposed method is capable of producing a higher accuracy model than any of the initial models by utilizing the potential of each of these input models in the fusion process.

The training data selection is an important and operative step in the classification and change detection procedure from remote sensing images, which needs to be provided with high sensitivity. These samples are often determined by the human factor, which is a time-consuming process and prone to high error. Old maps can be a valuable source of information for selecting and preparing training samples. If these samples are accurately refined, they can speed up, facilitate and also increase the accuracy of the change detection process. The main innovation of the present paper is the diligence in the sampling process, which has been made imaginable by proposing a model based on the chi-squared statistical test and k-means clustering. This method, while using Chi-square statistical test, tries to select pure training samples, by selecting samples that are close to the centers of internal clusters in each class with multiple k-means clustering that takes into account the internal spectral diversity of classes. In this method, the spectral and the first and second-order of co-occurrence matrix are extracted and used in the support vector machine (SVM) classification process. Furthermore, the feature selection and SVM parameters have been optimized by the genetic algorithm to more improve the classification and change detection accuracy. For implementation, bitemporal satellite images at 2011 and 2015 and land use map of 2009 related to the Shiraz has been employed. Using the proposed method led to update the thematic maps of the study area with an overall accuracy of 98.72% and 94.57%, and a from-to change map. Experimental results showed that the refinement process of the training samples improves the results of the 2011 image classification (increasing the kappa coefficient from 65% to 87% and increasing the overall accuracy from 73% to 91%) as well as the 2015 image (increasing the overall accuracy from 69% to 86.32% and Kappa coefficient has been increased from 59% to 80.48%).

Nowadays, cities’ population has been encountered with rapid growth. However, the urban transport infrastructures are not developed with this rate. This issue yield to the common problem which is the traffic jam in streets. The practical approach to solve this problem is the efficient use of the infrastructure and proper traffic control which plays an important role in this performance. Based on the aforementioned issues, the aim of this study is an urban traffic control system using the agent-based simulation approach. In this study, the microscopic model for efficient network traffic junctions in the context of the flow of traffic at intersections has been used. The proposed approach, by defining the main parameters of traffic as a collection of micro-scale smart agents, is expected to address the issue mentioned more effectively. These agents consist of three categories (vehicles, traffic control centers and traffic lights) with different functions. In our approach in addition to the defined scenarios for intersection with a focus on optimizing time, total optimization of traffic flow will also be followed. Also, path finding is used to test the performance of the model. Having the traffic lights with three phase and two methods of path finding, six scenarios are defined and are implemented in a simulated environment. The results of the comparison parameter stop time and the average speed of vehicles reveals the fact that using the intelligent path finding and smart traffic light (sixth scenario) would lead to the downturn about 3.12 seconds per kilometer for stop time and upturn near 1.83 km/h for average speed of cars using the proposed method. We used the controlled data for the evaluation process and the relative accuracy was 83% for stop time and 94% for average speed of vehicles. The results of this study revealed the efficiency as well as reliability of the developed agent-based model in traffic smoothing.

urban population, especially in developing countries, is increasing gradually. Optimal use of land plays an important role in planning and urban development management. This principle is especially important in achieving sustainable development in urban areas. Not-principles Exploitation causing destruction and degradation of resources. Study dynamics of land use changes and their broad impacts on the environment, it is essential to understanding how these changes occur, both in terms of spatial pattern and in terms of its quantity. In this article, it was noticed to simultaneously apply spatial information and remote sensing information to study and model land use change in order to Isfahan city. land use maps using Landsat images for the years 1997, 2008 and 2017 manufacturing and maps of 1: 25000 scalewere used to better identify the area, land reference and geometric correction of satellite images. Transmission potential modeling using perceptron multi-layered artificial neural network algorithm was down. Through certain dynamic variables include distance from academic centers, distance from industrial centers, distance from residential areas, distance from passages as well as digital elevation modelas a static variable; then, The amount of each land use conversion is predicted with the Markov chain and the total map of land use change made with two models of hard prediction and soft prediction in the LCM model.The results ofland use transformation potential modeling in all submodels showed over 95% accuracy. the calculation of accuracy of the prediction model, i.e. the kappa coefficient equal to 0.9 was obtained. The results obtained from the study of changes and prediction of land use indicate the development of urban areas Isfahan. These changes also indicate that decrease of other land use classes. According to the results of the LCM model over the whole span, the urban lands have increased from 21239 hectares in 1997 to 23607 hectares in 2017; this upward trend will continue into the future. In modeling and according to the results of the Markov chain model, the urban lands will reach 24023 hectares by 2027.

In recent years, the use of unmanned aerial vehicles (UAVs) that has been introduced to UAVs has been widely used in surveying engineering under the name of UAV photogrammetry. Different urban and non-urban scales on different scales provide the conditions for examining and evaluating the geometric accuracy of these high-resolution spatial images in the production and delivery of large-scale maps and coverage scales. One of the most important geometric parameters in UAV images is the determination of the spatial resolution, which is known as the criterion for detecting the smallest distance between two adjacent objects that can be distinguished in the images. There are several ways to accurately measure the spatial resolution of images; in this study, the Siemens star was used as one of the most widely used artificial targets in measuring spatial segregation. The purpose of this paper is to provide an automatic method for detecting the radius of ambiguity and calculating the spatial resolution limit in images taken from UAVs. The results of this study showed that firstly, according to the flight altitude and the amount of blurring of the image, the Siemens Star Target should be used with appropriate dimensions and number of arms, and secondly, the rate of reduction of image resolution in the tested drones was between 1.2 and 3.7.