نشریه علوم و فنون نقشه برداری
سال دهم شماره 1 (پیاپی 37، تابستان 1399)

The hyperspectral imagery provides images in hundreds of spectral bands within different wavelength regions. This technology has increasingly applied in different fields of earth sciences, such as minerals exploration, environmental monitoring, agriculture, urban science, and planetary remote sensing. However, despite the ability of these data to detect surface features, the measured spectrum is composed of several components that make it a mixed spectrum due to the low spatial resolution observed of the employed sensors or the presence of multiple materials in its instantaneous field of view (IFOV). The existence of a mixed spectrum severely prevents the accurate processing of the hyperspectral data. Therefore, it is necessary to separate these mixtures through the so-called spectral unmixing methods. Spectral unmixing is performed to decompose a mixed pixel in hyperspectral images into a set of spectra (endmembers) and their abundances. Typically, two types of spectral mixing models (linear and nonlinear) are considered. In the linear mixing model (LMM), the reflected radiance at the sensor is the outcome of interference with one material, where a pixel is assumed to be a linear combination of endmembers weighted by their abundances. The nonlinear model, on the other hand, is used when the mixing scale is microscopic or materials are mixed intrinsically. In recent years, the linear mixing model has been a very popular model for hyperspectral processing in the last decades, and a large effort has been put into using this model for unmixing applications, resulting in an overabundance of linear unmixing methods and algorithms. Over the last decades, the linear mixing model has been utilized in the detection of minerals and their abundances. However, as early as 40 years ago, it has been observed that strong nonlinear spectral mixing effects are present in many situations, for instance, when there are multi scattering effects or intimate mineral interactions. While such nonlinear unmixing techniques have received much less attention than linear ones.
Therefore, this paper aims to give an overview of the majority of nonlinear mixing models and methods used in hyperspectral image processing, and many recent developments in this field. Besides, several of the more popular nonlinear unmixing techniques are explained in detail. In this regard, nonlinear unmixing methods can be categorized into two groups: physics-based methods and data-driven techniques. The most important methods of these two groups are divided into bilinear and multi-linear models, intimate mineral mixture models, radiosity based approaches, ray tracing, neural network, kernel methods, manifold learning, and topology methods. A comprehensive review of these methods can be found in which bilinear and multi-linear models and neural networks have become more popular among researchers over the years. The current study should give the reader that is interested in working with nonlinear unmixing techniques a reasonably good introduction into the most commonly used methods and approaches.

Information about vegetation cover and their health has always been interesting to ecologists due to its importance in terms of habitat, energy production and other important characteristics of plants on the earth planet. Nowadays, developments in remote sensing technologies caused more remotely sensed data accessible to researchers. The combination of these data improves the object classification and recognition results. Recently, hyperspectral and Lidar data has been used for vegetation covers classification. The spectral information derived from hyperspectral data is used to classify and identify the vegetation cover. However; due to the spectral similarities between various vegetation types, false positive results are increased. Using relief information extracted from Lidar data can solve these kinds of errors and can be very efficient for improving the object recognition results. Spectral similarities and spatial adjacencies between various kinds of objects, shadow and occluded areas behind high rise objects as well as the complex relationships between various object types lead to the difficulties and ambiguities in vegetation recognition among other objects in urban areas. Therefore, new procedures and higher levels of modifications should be considered for improving the object recognition results. In recent years, the multi-agent systems have been considered as one of the most powerful tools for solving the problems of automatic object recognition in urban areas.

According to the difficulties of vegetation recognition in complex urban areas, the proposed object recognition in this paper is a decision level fusion strategy between hyperspectral and Lidar data based on the capabilities of the multi-agent systems. Vegetation indices from hyperspectral image are used as spectral features in the knowledge base. Moreover, digital surface model which is produced from Lidar data is used for height features extraction. After producing a rich knowledge base containing the spectral and height based features, the proposed hierarchical classification is performed which is composed of two steps; step 1: initial vegetation candidate recognition, step 2: vegetation classification based on the capabilities of the multi-agent systems. Applying the optimum thresholds on the normalized difference vegetation index in the first step produces a binary image containing the initial vegetation candidates. The multi-agent system in the second step of the proposed method in this paper contains several object recognition agents (one agent per each vegetation cover type), a coordinator agent and a yellow page. The object recognition agents have three layered internal architecture and use the belief-desire-intention (BDI) reasoning model. 

The capabilities of the proposed multi-agent vegetation recognition algorithm in this paper is evaluated based on the hyperspectral and Lidar data collected from the University of Houston and the surrounding areas. Four object recognition agents are defined for trees, healthy grass, water-stress grass and artificial grass. These four object recognition agents perform their reasoning based on the pre-defined spectral and height features in the knowledge base. The obtained results indicate the overall accuracy of about 87% from the proposed multi-agent hyperspectral and Lidar decision fusion strategy. The obtained results from performing the same multi-agent system only on the hyperspectral image (without considering Lidar data) have the overall accuracy for about 71%.

The ability of recording the high resolution spectral signature of earth surface would be the most important feature of hyperspectral sensors. On the other hand, classification of hyperspectral imagery is known as one of the methods to extracting information from these remote sensing data sources. Despite the high potential of hyperspectral images in the information content point of view, there are a numerous challenges in reliable extraction of information from these images. The issues such as 1- spectral similarity of different phenomena, 2- sensor noises and atmospheric effects, 3- the effects of high dimensionality in the pattern recognition algorithms, 4- the necessity of large number of training data to perform a reliable classification, and 5- spectral variability of similar phenomena could be considered as some of the challenges in hyperspectral data processing. Decreasing of the high dimensionality effects via the dimension reduction algorithms (e.g. band selection and feature extraction algorithms), as well as increasing the separability of the overlapped classes through the linear/non-linear mappings into the feature spaces with the higher dimensional are two opposite and conventional approaches of hyperspectral data processing. These approaches would be used based on the factors such as 1- complexities of classes in the imaging area, 2- spectral range of imaging sensor, and 3- the restrictions of processing algorithms. In this paper the fusion of these two approaches is used to perform an accurate hyperspectral image classification. To do so, a novel feature extraction method is proposed to be used in the hyperspectral image classification. The core of this method is the fusion of the linear, non-linear and sparse representation based features which is used to produce the effective features in the weighted K-Nearest Neighbors (KNN) classification method. In this procedure, a set of supervised and nonlinear features are extracted as the first step through the Nonlinear Principal Component Analysis (NLPCA). The supervised usage of NLPCA in order to extract features is known as one of the novelties of this paper. In this step, the spectral bands are usually mapped to a high dimensional feature space through the self-estimator artificial neural networks (ANNs) which are trained separately by ground truth data. In the second step, the previously extracted features are linearly transformed by the Linear Discriminate Analysis (LDA) method in order to reduce the dimension of the hypercube generated via supervised NLPCA to a separable feature space. In the last step, a set of features which is proportional to the number of classes is generated based on the sparse representation theory. The sparse representation features were hired to handle the effects of the inter-class variability. The precisions of the classified features in the two different hyperspectral images were on average shown 6 percent improvements in comparison with the spectral bands and the other combinations of extracted features. Furthermore, reach to the approximately 99% overall accuracies in the classes with the few training data could be considered as other achievements of the proposed method.

Land cover is defined as the physical material of the surface of the earth, including different vegetation covers, bare soil, water surface, various urban areas, etc. Land cover and its changes are very important and influential on the Earth and life of living organisms, especially human beings. Land cover change monitoring is important for protecting the ecosystem, forests, farmland, open spaces, water quality, wildlife habitat, and human health. Uncontrolled changes in land cover (deforestation, extensive agricultural activities, vast urban and exurban development, etc.) will not only destroy the natural environment and the ecosystem but also will affect different aspects of human life including the economy. As a result, various effective methods for land cover monitoring are always deliberated by researchers. Remote sensing based techniques, due to their unique capabilities and extensive data availability, have attracted great interest for decades. In addition, fully Polarimetric Synthetic Aperture Radar (PolSAR) images, due to the rich data and the ability of day-night and all weather condition data acquisition, has got high capability to study and monitor land cover changes. Recent developments of SAR sensors in acquiring fully polarimetric, continuous, and very high resolution data, besides enabling precise land cover monitoring, has necessitates the usage of more powerful and robust algorithms in different image processing steps. Classification algorithms are among the most fundamental tools of remote sensing image processing; and an increasing number of researches have been focused on developing novel and more robust classification methodologies for remote sensing data in recent years. Mid-level representations have also been introduced to the remote sensing data classification to enhance the reasonability and semantic rationality of the land cover classifications through remote sensing techniques. Bag of Visual Words (BOVW) model, originally inspired by the Bag of Words (BOW) model from text mining, is one of the most authoritative mid-level representation models which has achieved state-of-the-art results in different image processing tasks. BOVW representation model has recently been introduced to remote sensing data processing and proved to be very beneficial for high resolution remote sensing data representation. In the present paper, a novel segment-based BOVW framework has been developed for PolSAR data representation and classification. The obtained classified maps are quantitatively and qualitatively compared with the result of classification with well-known classifiers including Support Vector Machine (SVM), Artificial Neural Network (ANN), and Wishart Classification Algorithms. The main contributions of the current study are (i) covering the semantic gap between low-level features extracted from the PolSAR image and high-level concept of land cover in remote sensing data classification (ii) introducing novel segment-based BOVW framework for PolSAR image classification, and (iii) extensive evaluation of the performance of the BOVW model for PolSAR data classification in terms of the model parameters and low-level features selection. The experimental dataset is acquired by RADARSAR-2 from San Francisco Bay, California, in C band and at fine quad-pol mode, in 2008. The achieved overall accuracy, 90.1%, illustrates the capability of the proposed model for SAR image classification purposes. Moreover, the proposed segment-based framework for the BOVW model has decreased the undesired speckle effect of the SAR data, without speckle filtering.

Urban growth is a dynamic and evolutionary spatial and social process that relates to the changes of urban spatial units and the transformation of people’s lifestyles and consequently demographic changes. Considering the urban development process as a function of land uses interactions, population structure and the strategic behavior of the agents involved in the urban development process (the Developer as the investor and the Municipality as the planner and regulator), asking for developing a framework to capable modeling and analyzing these driving actors. This study is presented a model based on the integration of cellular automata, multi-criteria analysis and agent-based Game theory for land use planning and simulation of urban development using two urban population changing scenarios. This integration is used as a tool for modeling and quantifying land uses interactions with respect to their service region. Moreover, in the proposed model, land-use type change is defined as the outcome of the interaction between two decision-makers (agent): Developer and Municipality. Game theory is used to model and quantify the strategic behavior of these two agents. The developer’s strategy is to develop spatial units that have more suitability due to their neighborhood externalities and current demand for each land use type while the municipality’s strategy is to protect the zoning regulations based on the master plan and land use suitability. The utility functions are defined based on each agents’ preferences, the game structure for each spatial unit is composed based on developer priorities (as proposer) and the bargaining situation between agents involved in the development process (as a sequential game) is solved using backward induction process. Utilizing Game theory will result in finding an optimal and rational strategy for both agents in developing land-use types for each permissible spatial unit. The methodology presented in this study proposes a rational and realistic approach for urban land use planning and simulating urban growth. To evaluate the performance of the proposed model, it was implemented in the region of Roozieh in the city of Semnan. Two scenarios were modeled based on the Semnan’s approved master plan. The suitability of developed land-use types is evaluated using multi-criteria analysis. In order to provide a more efficient evaluation method, the outcome of the implementation of each scenario using the proposed method was evaluated to provide a basis for comparing results based on the concept of spatial equity. The results demonstrate the ability of the model to simulate the outcome of urban land-use development plans based on the regulations and guidelines of the master plan, especially for the balanced distribution of services and service availability.

By today, the technology of synthetic aperture radar (SAR) interferometry (InSAR) has been largely exploited in digital elevation model (DEM) generation and deformation mapping. Conventional InSAR technique exploits two SAR images acquired from slightly different angles, in which the information of elevation and deformation can be captured through processing of the phase difference of the images called interferometric phase. Depart from undeniable efficiency of interferometric SAR processing technique (InSAR), some main issues such as phase unwrapping ambiguity may limit its applications and its accuracy in height mapping. However, in the frame of multi-baseline interferometry and by the availability of more than one interferogram of the same region these problems can be overcome. Multi-baseline SAR interferometry are hence of great interest and can be successfully exploited for automatic phase unwrapping and high quality DEM reconstruction. This paper focuses on stacks of interferometric SAR data as they are used as input to multi-baseline framework for the purpose of height estimation and compare the results of such local and non- local covariance matrix estimation methods achieved by same data and on the same area, where the information of estimated covariance matrix is employed in the elevation mapping. In local methods such as Boxcar, a fixed-size window is considered for the central pixel which do not consider the statistical homogeneity of neighboring pixels, so this method in non-homogenous area leads the results to lower accuracy. In non-local methods the procedure is centered around the idea of checking the pixels to find the same statistical distribution as the investigated pixel, which is realized by Wishart similarity function. In this case, all the similar pixels are then used to estimate the complex covariance matrix of the reference pixel. In the context of non-local filtering, one of the most efficient method is NLSAR approach, which has been considered in our framework. More precisely, NLSAR uses samples in a search window and assigns each pixel a weight based on its similarity to the target pixel. The idea of NLSAR approach is to find, within a search window, for each pixel p to be filtered non-local neighbors t that share statistical similarity with the considered pixel. A pixel t is assumed to come from the same statistical population as the considered pixel p, if the patches or local neighbors that surround the two pixels are similar. The similarity for two pixels is defined as a likelihood-ratio test based on the hypothesis that their two Wishart distributed covariance matrices are equal. The main peculiarity that made the NLSAR approach extremely popular is its ability of filtering noise while preserving structures and discontinuities. The stadium height estimated by using the covariance matrix estimated with Boxcar and NLSAR methods respectivey is equal to 41.100 and 42.5400 meter which in the comparison with the actual height extracted from AfriSAR mission, indicates higher accuracy for the results of NLSAR method. The task of covariance matrix estimation is so challenging for complex area such as the area used for this paper which contains the Angondjé stadium in Mondah, Gabon, that represented a complex scenario because of the occurrence of layover, a phenomenon that gives rise to the interference within the same pixel of ground scattering mechanisms located of different height with  same slant range distance. we use PCA (Principal Component Analysis) to decompose principal scattering mechanisms. Then , the powerful statistics Maximum Likelihood (ML) technique is used to properly compute the elevation information of the principal components by the available information of covariance matrix. From this covariance matrix, both amplitude and interferometric phase values extracted which are then used for height estimation.

Panchromatic and multi-spectral images produced by the remote sensing satellites are fused together to provide a multi-spectral image with a high spatial resolution at the same time. The spectral quality of the fused images is very important because the quality of a large number of remote sensing products depends on it. Due to the importance of the spectral quality of the fused images, its evaluation is also important. This paper presents an object-based strategy for evaluating the spectral quality of fused images, aiming to overcome the limitations of the current pixel-based method. This type of assessment is conducted by focusing on homogeneous objects with similar spectral and textural behaviors. In the implementation phase of the article, after determining an optimal spectral metric, the proposed object-based strategy is applied to five datasets from four different satellite sensors types, and the spectral behavior of the fusion methods has been studied in several image classes. The results indicate that the spectral behavior of the fusion methods does not follow a deterministic rule. Finally, statistical analyses were used to determine the best fusion algorithms in each class, and a list of superior algorithms in different classes was provided to researchers in the field of remote sensing and image fusion. This approach will help the scientific community to take a realistic vision at choosing the fusion algorithm appropriate to their satellite imagery.

Hyperspectral data in Remote Sensing which have been gathered with efficient spectral resolution (about 10 nanometer) contain a plethora of spectral bands (roughly 200 bands). Since precious information about the spectral features of target materials can be extracted from these data, they have been used exclusively in hyperspectral target detection. One of the problem associated with the detecting process using hyperspectral data is the spectral variation due to topography variability and spectral mixing. Moreover, imperfect sensor noises and atmospheric influences on the target radiance together lead the observed spectral feature of the same material to change in different situations. Target detection methods model the spectral variation in order to compensate their effects on the process. Statistics and subspace based approaches are the two most important methods used in detection process. Statistics and subspace based approaches are the two most important methods used in detection process. Using special statistical assumptions and modeling the spectral variation with limited number of parameters are the main disadvantages of these methods. One of the strongest detection method is the sparse representation method. It models the differences in the spectral features of targets and background using dictionary matrices. Indeed, it constructs a complete subspace of materials spectrum and their variations. Building a pure dictionary (clean of spectral mixing) is the main challenge associated in the sparse representation method in the detecting process. Three methods- the dual windows, the global and the learned dictionary- have been introduced in literature. In the dual windows, since it uses outer window to select the target pixels, spectral mixing has not been cleaned. In the learned dictionary as it uses random picked pixels in order to learn the dictionary, the risk of spectral mixing exists. Furthermore, spectral mixing exists in general method. Considering the disadvantages of the aforementioned methods, in this thesis we introduce a new method to construct the dictionary. Not only do the dictionary atoms provided by this method construct a complete subspace and model spectral variation, but they also are as pure as possible.  In the proposed method, it is tried to achieve two main purposes which are forming the background subspaces and minimizing the spectral mixing of atoms in the dictionary and target. To this end, correlations between target spectrum and all image pixels are calculated. Afterwards, using image pixels which have different degrees of correlation with target spectrum, different dictionaries are created for the background. Finally, a dictionary is selected from the created dictionaries which presents a complete subspace of image and the subspace also has the lowest correlation with the target spectrum. In this paper, the proposed method of making a dictionary along with a sparsity model, called SRBBH is used and introduced as method Proposed+SRBBH. To survey the efficiency of the proposed methods, a simulation data set and three real data were used, and in order to evaluate the methods, the area below the ROC chart level was used. In experiments performed on both Cuprite and Sandiego data, the area under the graph was 0.9997 and 0.9961, respectively, which shows higher values than other methods. For the other two sets of data, the proposed method performs better than other methods of target detection.

Modeling a road accident hazard zoning map to identify high-risk areas is a very effective step to reduce the resulting casualties. Due to the dynamic nature of many of the factors affecting the identification of these areas, traditional zoning mapping does not seem to be effective. In the field of ubiquitous modeling in the framework of the GIS, it is possible to produce a separate map at any time, any place, for any user and under any circumstances that is more compatible with his or her changing individual and environmental conditions. In this study, in a hybrid model of data mining methods based on 22 environmental and individual contexts the probability of accident risk was calculated for each user. Thus, by obtaining the accident data recorded in Tabriz Marand road in 2019, the required pre-processing was done based on T2 statistic and PCA method. Then, based on GRNN and the collected data, the optimal network was trained and then evaluated with test data. In addition, eight different scenarios were designed for this case study and at 3008 points of this road, the risk of accident was predicted for each scenario. The results showed more than 90 % accuracy for the proposed model of this study. According to the results of different scenarios, the 5 km area around customs and 3 km around Sufian city have the highest risk in this road, which can be verified by 50 accidents happened in this area during the mentioned period.

One of the consequences of a fire is smoke. Occasionally, monitoring and detection of this smoke can be a solution to prevent occurrence or spreading a fire. On the other hand, due to the destructive effects of the smoke spreading on human health, measures can be taken to improve the level of health services by zoning and monitoring its expansion process. In this paper, an automated method is proposed to detect the dilute smoke caused by large fires in multispectral images. The main idea of this method is the impossibility of precisely reconstructing the smoke in the bands affected by smoke (blue band) using regression models from other spectral bands. In the first step of the proposed method, the absolute value of the residuals of the regression estimation of blue spectral band is transformed into a binary mask with the help of Otsu thresholding. Afterwards, in an iterative process, non-smoke areas are detected and then clustered. In the iteration process, a regression model is fitted for each cluster and for each pixel, coefficients with the least error of the blue band reconstruction is used. Through more accurate estimation of the blue band, it reduces the effect of First Positive Error and leads the mask of residuals obtained from thresholding process to the smoke areas. The final step of the proposed method is to refine and remove the incorrect image segments. This method has been successful in detecting diluted smokes and also in disregarding smoke in non-smoky images. The results show the average accuracy of  99.04 percent in several datasets with diluted smokes.

In recent years, rapid population growth has led to increase per capita water use in various sectors including agriculture and industry and a growing gap between water demand and water supply has emerged. Therefore, identifying and tracking changes in groundwater resources as an alternative and reliable source of surface water resources are so important to region located in the Middle East with dry weather and large volumes of drought and climate changes. In the current study, the potential and evaluation of water resources are investigated in the west and northwest formations of Shahroud in an area of about 480 square kilometres using remote sensing and spatial information techniques. In this region, due to the presence of carbonate rocks as well as the function of erosion and tectonic forces in parts of the region, relatively suitable aquifers have been formed. For this purpose, information layers including lineament, lithology, slope, aspect, waterways, precipitation and type of precipitation were evaluated by Analytical Hierarchy Process (AHP) method. In this study, information layers were prepared using the ability of RS and GIS in three main stages: extraction of geological map, extraction of area lineaments and extraction of other information layers. The proposed methodology applied two different types of remote sensing sensors including Landsat 8 as optical data and Sentinel-1 as radar data. In preparing lithology map with emphasis on calcareous formations, the proposed method applied four techniques including independent component analysis (ICA), minimum noise fraction (MNF), band ratio (BR) and color composition on Landsat 8. Using the pixel purity index, the endmemberes were extracted and the obtained maps were classified by Support Vector Machine (SVM) and maximum likelihood (ML). Among the classification methods, SVM has a higher ability to classify than ML and identified formations with higher accuracy in the region. Finally, tree decision making system was used to improve the classification of images. The geology of the area was extracted in four classes focusing on calcareous formations. The map was compared with the geological map prepared by the surveying organization and the following results were obtained: Kappa coefficient 0.83, Accuracy of Ku and Jd Formation (calcareous formations) with 99.2% accuracy, Shemshak Formation (Js) with 80.2% accuracy, Lalun Formation (Cl) with 81.2% accuracy and Alloviume (Q) with 78% accuracy were identified. Subsequently, lineaments area were extracted using integration of Landsat 8 and Sentinel 1 radar remote sensing data based on semiautomatic methods. Based on the results, the lineaments were in good agreement with the faults in terms of orientation and numbers at different lengths. Also, the densities of the lineaments extracted in different formations of the geological map of the region had 99% compliance with the densities of faults in the formations. Therefore, by combining band6 of Landsat8, VV and VH polarization Sentinel1, the area lineaments can be extracted with high accuracy. Other thematic layers were extracted by using remote sensing and GIS Techniques using 30 m SRTM. It was also observed that the groundwater potential map is mainly controlled by precipitation, lithology, and lineament density factors.

Universities as the places of training the experts play an essential role in social, economic and cultural structure of the nations. The quality of education and the educational space are two major effective factors on the quality of training the students. Since the educational places are of the most important land-uses in the cities, the compatibility of them with other surrounded land-uses is vitally important in urban planning. In many cases the adjacency of educational places with incompatible land-uses such as military and industrial or locating in polluted areas or avoiding from necessary utilities have caused falling the learning abilities of the their students. In this research the desirability of the location of six universities of Tehran namely: K.N.Toosi university of technology, Iran university of science and Technology, Amirkabir university of technology, university of Tehran school of engineering, Shahid Rajaee University and Allameh Tabatabachr('39')i university has been evaluated and compared using the spatial analyzes in Geospatial Information System. To find the major criteria (factors), many students in those universities were asked. Based on the responses, the criteria were classified into two categories of physical and environmental. Satellite images, maps and census data were used to produce criteria maps. Evaluating and comprising between the criteria were done based on the idea of more than 500 students. Each of considered factors is extracted from a corresponding factor map and this is the point that highlights the role of GIS. For environmental factor three maps were produced and then combined. Greenness map in this study is a map showing the NDVI index of Tehran. To produce this map, image of Landsat 7 satellite, ETM+ sensor, band 3 and 4 was used as the input. ENVI software was used for this process and NDVI obtained within a thousand meters radius around each university. For the other map processes in this study ArcGIS 10.3 software was used. Next, three factor maps were combined using Index overlay method with the equal weights and environmental index map was created. Air pollution is at the highest level in winter. Noise pollution is significantly higher in the mornings. Real greenness is better observable in summer when all the trees have leaves. Different time of gathering will not affect the results because the relative conditions of the target universities are fairly constant during the process of collecting data. To prepare physical index map, accessibility and compatibility of surrounded land-uses must be determined.  Accessibility was extracted from a 1:2000 map of Tehran using OD-cost matrix in ArcGIS software. To generate compatibility, land-uses around the target universities were determined and their compatibility with university land-use was compared pair-wisely. The compatibilities expressed in five levels. Levels of compatibility have then been assigned different weight based on previous studies. Once the values of factors obtained, they were be combined to get the final index. However, the weights of each criterion must be assigned. AHP as a multicriteria decision making method and ratio estimation method were used for this task. At last, the final index for desirability of the location of target universities achieved by combining sub-criteria. The results of this research revealed that the location of Amirkabir University achieved the more desirability of location. After that K.N.Toosi, university of science and technology, university of Tehran school of engineering, Shahid Rajaee university and finally Allameh Tabatabachr('39')I stand on the next ranks.

Land use change monitoring is one of the main concerns of managers and urban planners due to human activities and unbalanced physical development in urban areas. In this paper, a combination of remote sensing data and volunteered geographic information was used to assess the quality of volunteered geographic information on land use and land cover changes monitoring. For this purpose, the ORBVIEW-3 satellite imagery for 2005, the IRS satellite for 2008, 2010, and 2015 and the Google Earth imagery from 2011 to 2019 have been used as the reference database. One of the issues discussed in the field of validating volunteered geographic information in monitoring land-use changes is related to the quality of this information. Various indices have been proposed for evaluating the quality of VGI parameters in the field of validation of land use changes monitoring, includes data completeness, spatial accuracy, and shape accuracy. This paper aimed to evaluate the quality of volunteered geographic information designed for land use changes monitoring and compare the results with satellite images as reference data. In other words, this study seeks to compare the results of volunteered geographic information and satellite imagery to show the accuracy of VGI . Actually, the use of local knowledge of individuals in the urban planning process and the importance of timely information for better management and decision-making in the face of urban progresses such as urban sprawl has led to attention and understanding the importance of VGI in land use change. To validate the VGI, first satellite images were obtained and clustered using unsupervised learning. The use of satellite imagery due to the repeatability with wide and multi-source view of an area increases the credibility and confidence of VGI quality validation. The VGI collection has been carried out over a web-based GIS which provided a facility for users to register personal information, parcel land use and draw polygons per relevant land use. Finally, volunteered geographic information quality was evaluated. In the first step of validation process, data completeness was assessed. According to the results, the value of VGI completeness has been changed during assessment period, as  by moving away from the present, the accuracy has been also reduced, so that in 2018, 72.7 percent of the volunteered data were completely overlapping with the reference data, but in 2007 this compatibility decreased to 25 percent. In the next step, the spatial accuracy of the data was calculated. The central distance method was used for evaluating this parameter. The results of the distances between the polygons centers of the volunteer data and the polygons centers of the reference data show that the maximum and minimum distance are 14.37 m and 0.09 m, respectively. In the last stage, the shape accuracy of the volunteered data was examined. Due to the standard deviation of 0.61, it can be concluded that the two databases differ significantly.

With the increasing use of spatial data in daily life, the production of this data from diverse information sources with different precision and scales has grown widely. Generating new data requires a great deal of time and money. Therefore, one solution is to reduce costs is to update the old data at different scales using new data (produced on a similar scale). One approach to updating data is to use the updated large-scale dataset as reference data to update small-scale datasets; in this way, the modified features are identified in the two datasets and then updated by modifying the changes to fit the small-scale dataset. In terms of the type of the updated feature, map updating issues in the vector dataset are divided into three categories: pointwise, linear, and polygon. One of the most important features of the class of polygonal features in urban environments are the buildings that are vital in urban maps and their updating process in urban applications has a high priority. In this research, an attempt has been made to study the issue of updating polygonal features from different perspectives by a careful and comprehensive examination. These perspectives include spatial clustering methods, pattern extraction, and updating methods. 
Spatial clustering methods are classified into five

natural principles, partition-based, graph-based, Region Merging, and density-based approaches. Each clustering method have been used in different studies according to Gestalt criteria (proximity, similarity, and continuity). In pattern extraction, different types of patterns have been studied in various studies while the linear pattern extraction as a sample has been comprehensively examined in this study.  In the updating methods, three updating approaches including Propagating, Local, and Constraint-based are examined. In the Propagation updating approach, only large-scale data are updated, then these updates can be propagated into small-scale data. This update is especially used for the MRDB spatial database by propagating updates at various scales. Local updating consists of three steps: 1) Change detection between the recently updated large-scale dataset and the old small-scale dataset. 2) Integrating the discovered changes into the small-scale dataset (by quantifying and formulating these changes). 3) Ensuring that the consistency is maintained. In Constraint-based updating approach which demonstrates the necessities of this research, first, by grouping the buildings, useful information such as the area of ​​the buildings, the average and the standard deviation of the separation distance between the buildings is obtained. Then, with the appropriate operators, Constraint-based generalization is performed to update the maps.  There are various criteria for evaluating the updating methods, while the Precision and Recall criteria have been used to evaluate the effectiveness of these problems. Precision criterion examines the ratio of the number of correctly matched features to the total number of features that that have been matched correctly or incorrectly. Recall criterion evaluates the ratio of correctly matched features to the total features that have been matched correctly or not matched. In other words, in the Precision criterion, the number of features that are incorrectly matched and in the Recall criterion, the number of features that are not matched, are effective in comparing the two criteria. The categorization mentioned in each perspective and the advantages and disadvantages of different methods are also presented in this research.

Nowadays, given the widespread use of global projection systems such as the UTM projection system in surveying projects relying on elliptic referents, finding a suitable composite scale factor for each project to implement construction projects using mapping equipment such as devices Total station is necessary. Unfortunately, today the method of calculating and assigning average composite scale factor in survying projects is not fully scientifically performed, and usually inappropriate composite scale factors are calculated and applied, causing unacceptable errors in the project. In this research, an executive project was investigated and a number of scaling factors were calculated for it. The results showed that the method that calculated the scale factor by considering the whole project scope in three dimensions yielded the most accurate case. Evaluation of the accuracy of work showed that the highest error was related to the average scale factor in a thousand meters length, 0.048 m to the east and west end of the project and 0.002 m to the center of the project. It was also found that if the permissible error of the mean scale factor is 5 mm, about 1266 m can be survey from the project center east and west with the same mean scale factor and if more than That range of surveying activity should be done, either more error must be accepted or another average scale factor calculated for the new range.