نشریه علوم و فنون نقشه برداری
سال نهم شماره 2 (پیاپی 34، پاییز 1398)

Reverse geocoding is the process of assigning a readable place name or address to a point location. Common reverse geocoding methods assign the shared location to the closest venue based on Euclidean distance. In recent years, due to the advancement in positioning technology, a huge amount of spatial data has been generated by location-based social networks such as Yelp and Swarm. Additionally, various services offer the ability to provide online weather data in any coordinate and time. These data can be a valuable source of behavior patterns of different people in different weather conditions. Our study efforts to enhance the reverse geocoding based on spatial distance with the help of these data. In this way, weather condition data were used to make behavior patterns of people and check-in data were collected with the help of Swarm service. Swarm service which was used in our study is a new service from Foursquare that enables gathering check-in through the Twitter Streaming API. After gathering each check-in with Twitter streaming API, Weather data were provided instantly using the OpenWeatherMap API. Weather data were included various attributes that four of them were used in our study. Weather data were used in four categories, including; air humidity, air temperature, wind speed, and cloudiness to produce weather semantic signatures. In our study, linear, rational and sinusoidal functions were used for distorting the spatial distance with weather check-in probability in the process of reverse geocoding. In addition, two training and test data sets have been used in our case study (New York State) to specify the values of the model parameters and to evaluate the result. For the training process of location distortion functions, the check-in data were collected for New York State for one year from 01/03/2017 to 01/03/2018. The results showed that with the linear model and weather semantic signatures, the reverse geocoding results (based on spatial distance) of MRR and First Position indices (New York State) could be improved by 18.64% and 111.49%, respectively. For the process of evaluating linear location distortion function, the check-in data were collected for New York State for seven days from 01/03/2018 to 07/03/2018. The results showed that the reverse geocoding results (based on spatial distance) of MRR and First Position indices (New York State) could be improved by 13.40% and 66.96%, respectively. These results indicated the high capability of the presented model to be used outside of the timeframe of training data. In our study, one of the important challenges in the geolocation services, named the reverse geocoding process, was investigated. The model presented in this study was able to modify the distance between individuals and venues by linear location distortion function. Given that, this model has demonstrated its ability to be used with weather (and temporal) semantic signatures. It can be expected that future studies use other contextual data by location distortion functions.

Management and exploitation in mines require a continuous and relatively smooth surface of the mineral grades. While assessing the various mineral elements, the scattered exploratory cavities are irregularly excavated. Producing a continuous surface from measured data requires interpolation methods. Several factors, including the characteristics of the data, affect the efficiency of the interpolation methods. For this reason, the efficiency of different methods in various cases is inconsistence, and choosing the appropriate interpolation method is also challenging. Interpolation methods can be categorized into two groups of mesh-based and meshless methods. Despite the efficiency and capabilities of meshless methods, they have a fundamental shortcoming due to the fixed size of the support domain. On the one hand, the distribution of exploratory cavities in mines is usually irregular, and in some areas, it is very dense, and in others, it is very sparse. On the other hand, the grade values of minerals at the surface of the region can be very variable with high changes. Conventional interpolation methods do not have sufficient efficiency and flexibility in confronting these two aforementioned issues. In this study, a precise, reliable, and flexible method is developed for interpolation of minerals through integrating the moving least squares and recursive least squares methods. In the proposed method for crack detection, the residuals statistical test of least squares computations is used.  In this method, for the central point, a continuity threshold (non-continuity) is determined based on the standard deviation of field values, so that points with crack are revealed and removed from the calculation of the value of the central point. Moreover, the size of the support domain is determined dynamically based on the recursive property of the method. In this method, an individual radius for the support domain is assigned to each central point according to the values and distributions of the surrounding field points. The dynamic size of the support domain allows a precise and reliable estimation of polynomial coefficients and the values of the central points. The efficiency of the proposed method is evaluated by applying it to simulated data as well as comparing it with the results of conventional interpolation methods on real mineral data. The results of the simulation data indicate the ability of the proposed method to reveal the non-continuity and fractures of surfaces with determining the dynamics size of the support domain based on the data structure. To compare the results of the proposed method with conventional interpolation methods including LPI, IDW, Kriging, and RBF, the root mean square error (RMSE), mean and median of errors are used. In this way, in addition to the overall accuracy of each method, the distribution of errors is also determined. The RMSE, mean and median errors of the proposed method, using the 10-fold cross-validation method for chromium (Cr), are 28.020, 0.2.201 and 2.874, respectively, and for iron (Fe) are 1.074, 0.017 and 0.094, respectively. Comparison of these results with conventional interpolation methods indicates the efficiency of the proposed method for both groups of high concentration and significant changes in the values and low concentration and almost uniform level of values. The results indicate the ability of the proposed method in detecting the jumps and non-continuity in the support domain and removal of some field points within the dynamic process, lead to a significant increase in the efficiency of the method compared to conventional methods.

The discovery of patterns of human movement in inner-city environments is one of the most important parameters in studies such as urban planning and geospatial studies. One of the sources that are widely used today to explore patterns of human movement is movement-based social media data. These media provide a huge amount of data in two dimensions of time and space. The purpose of this study is to explore and survey the hidden patterns of human inter-urban movement based on movement data derived from human daily activities in the process of sharing information on location-based social media and taking into account the semantic dimension of the data. In this study, movement data from the foursquare social media is used to provide a semantic dimension to the data. In order to discover the hidden patterns of human inter-urban movement, the capability and efficiency of spatial-temporal autocorrelation analysis have been evaluated. In this research, using statistical analysis and considering the time dimension in the first stage, a significant process of changes in the spatial-temporal autocorrelation of the studied data is revealed with respect to the urban subdivision based on Thiessen polygonization method. Secondly, the problem of the trend of spatial-temporal autocorrelation changes and the relationship between information sharing, location and urban area at different times of day, in order to extract precise intra-urban movement patterns using semantic clustering of location-based data has been examined in the most prominent patterns of urban movement in different time periods. The results of this study demonstrate the high capability of spatial-temporal autocorrelation analyzes based on the semantic dimension of movement data derived from foursquare social media in discovering hidden patterns of human movement at the urban level.

Remote sensing satellite data has been widely used as a source of information for geologists on a regional scale. Detecting lineaments by remote sensing in desert and semi-desert areas where bedrock is fully visible can provide better results. Two types of lineaments are usually distinguishable by remote sensing data, namely: 1. Positive lineaments, including ridge and dyke bumps, and 2. Negative lineaments consisting of seams, cracks and faults. The purpose of this study is to detect the lineaments associated with the Fault, one of the active faults in central Iran, using optical, radar and altimetry data.

By examining the different bands of Landsat 8 satellite in order to select the appropriate band for extraction of lineaments, it was concluded that the shorter wavelength due to more penetration and better interaction with the ground surface phenomena would be more accurate. As a result, bands with wavelengths close to the wavelengths of Band 2 are used. After the necessary preprocessing, the filtering operation was performed, local sigma filtering was applied to all images (Asher, Landsat 8, DEM 12.5m and SRTM 30 m). The local sigma filter uses the local standard deviation calculated for the filter box to determine valid pixels within the filter window. This filter replaces the pixel value with the average calculated from valid pixels inside the filter box. In addition, Li filter were applied on radar images (Sentinel 1and Alos Palsar). In the present study, the automatic extraction of lineaments is based on two main calculations: first, the use of filters to detect edges, second, the information that gives us sudden changes in the value of neighboring pixels. Usually it is related to lineaments. The second stage reveals the lineaments

In general, for fault detection, radar images are better than optical images. The DEM 12.5 m had the best accuracy among the other data sets. Among the optical images, Landsat 8 OLI sensor data with 30 m spatial resolution was more capable of fault detection. Sentinel-1 images in C band is more capable than Alos palsar L-band radar images. In the northern sections of the fault, the eastern plate of the Dehshir Fault, show an uplift. In the southern part of the fault the western plate of the fault is uplifted. The Dehshir fault moves in both horizontal and vertical directions.

In this study, using the remote sensing data (optical, radar and digital elevation model), the Dehshir Fault, which is an active strike-slip fault, is detected. Remote sensing data are particularly important in radar extraction for geological and geomorphological applications. Radar data have been able to identify fault lines in almost all parts of the area due to their better interaction with surface phenomena. Optical data is not well capable as radar images for extracting fault line. By combining remote sensing techniques with fieldwork, you can achieve desirable results with lower cost and better accuracy.

Plants paly a very important role in creating and maintaining the biological balance which is vital for the life of each living creature including humans. Due to the great importance of vegetation cover in terms of habitat, energy production and other important characteristics on the planet, the recognition and monitoring of various plant species has always been a concern for ecologists and decision makers from the economic points of view. This will be possible on a large scale with remote sensing technology and the use of satellite images containing vegetation and their classification. Different techniques of classification are deployed where in some of them, use have been made of reflectance curves and their derivatives. In some other methods, some coding system applied on reflectance curves and their derivatives are used as a fast method.  In this work, a method named CBOSE is presented in which, a coding approach on reflectance and its derivatives is applied. The CBOSE method is coding based on extreme points of the reflectance spectrum and combines from one to several bits to distinguish between plant species with relatively high spectral similarity. This coding method, after necessary pre-processing such as water vapor correction and continuum removal analysis, on AVIRIS hyperspectral images of Indian pine region containing various species such as wheat, barley, alfalfa, grass, tree, soybean and corn were also applied in three stages of germination, medium growth and full growth. Then, the features with the highest separability between the classes were extracted and the classification was done on the properties derived from the codes by selecting the training samples. The classification output of CBOSE was compared with the result of classification by classifiers Support Vector Machine (SVM), Maximum likelihood (ML), Spectral Angle Mesure (SAM), and Hamming similarity criteria and with those of field data. Also the methodology of CBOSE was evaluated and compared with those of coding methods such as Spectral analysis manager (SPAM), Spectral feature-based binary coding (SFBC), Spectral derivative feature coding (SDFC), and Spectral feature probabilistic coding) SFPC). The results show that the CBOSE methods on the average performs respectively 20, 16, 11 and 7 percent better compared to the afore-mentioned methods. Also, in order to evaluate the effects of using derivatives in the coding process, all aforementioned procedures were repeated without using derivatives in the coding processes. It showed that on the average, deployment of reflectance derivative would 8% enhances the accuracy in classification.

Land surface temperature (LST) is one of the most important variables required in environmental and climatological studies. In order to calculate LST, accurate emissivity is needed. Recently, several methods have been developed to calculate LAST and emissivity. Some of these methods estimate LST based on a pre-known emissivity, while the others calculate LST and emissivity, simultaneously. LST mapping in urban areas can be difficult due to the high variation of the land cover and the formation of mixed pixels. Accordingly, the LST calculation based on the emissivity derived from a single method can be erroneous, especially using a low spatial resolution image in the urban areas. Integration of the emissivity values derived from different methods seems to be an effective solution in this situations. In this study, LST was calculated using Split Window and Planck Law methods for Tehran city. Three different methods including classification, normalized difference vegetation index (NDVI)-based method, and normalization emissivity method were applied to derive emissivity from MODIS images. NDVI-based method is a common method used NDVI thresholding to determine the emissivity of different pixels. In classification method, each pixel is classified into one of 14 classes for which the emissivity is known. Normalization emissivity method assumes a constant value as emissivity for a pixel in different bands to calculate temperature for these bands and then the maximum temperature derived through, is used for calculation of emissivity coefficients which are used for actual LST calculation using Planck function. In addition, MODIS emissivity product (MOD11A1) was used to compare with the emissivity derived from the other methods. In order to implement this study, the remotely sensed data including Landsat-TM data acquired in 2010, and MODIS products (MOD021KM, MOD05, MOD11A1) acquired in 2012 to 2013 were downloaded. Temperature data measured by three meteorological stations around Tehran were provided to validate the results. In order to integrate the emissivity values, averaging and median methods were used to fuse the emissivity values derived from three methods and MODIS emissivity product. The results showed that NDVI-based method produces more accurate emissivity as the LST calculated based on this emissivity was more accurate than that derived from other emissivity values. Fusing the emissivity values through mean and median methods, the fused emissivity values were used for calculating LST using Planck’s equation and Split Window methods. It was shown that the fused emissivity derived from averaging method can improve the accuracy of the LST maps derived from each emissivity method. Moreover, Planck Law performed better for calculating LST using MODIS bands 31 and 32 with error of 1.6 and 1.63 Kelvin degrees, respectively, compared to that derived from Split Window method.

Generating the accurate and real time information on the position of urban objects is essential for the management, planning, and automation of three-dimensional modeling of urban lands. Trees and low altitude vegetation cover (shrubs and meadows) are the most important urban objects because they play an important role in sustainable urban planning and development and environmental management and affect the urban temperature, air quality and noise levels in the urban environment. For this reason, in recent decades, identification and detection of trees low altitude vegetation cover in urban areas using remote sensing data has become one of the important research. So, in this research, a method is presented to identify trees and low altitude vegetation cover from aerial images with high spatial resolution and aerial laser scanning data. For this purpose, the first Orthorectified images of the three study areas were generated from aerial imagery and the noise in the LiDAR data was identified and eliminated. Then, Digital Elevation Model (DEM) is generated using a developed method based on the Scan Labeling Algorithm (SLA). In addition, normalized Digital Surface Model (nDSM) has been obtained by differentiating the Digital Elevation Model (DEM) from the Digital Surface Model (DSM). In the following, high and low altitude areas of the study areas have been identified by thresholding on the normalized Digital Surface Model (nDSM). Then, an Enriched Vegetation Index (EVI) in shadow areas was produced from aerial image to separate vegetation and non- vegetation areas. Eventually, trees and low altitude vegetation cover identified by overlapping the vegetation areas with high and low altitude areas, respectively. In this research, detected trees and low altitude vegetation areas evaluated by Working Group IV, Commission III of International Society for Photogrammetry and Remote Sensing (ISPRS-WGIII/4). In this study, average pixel-based completeness, correctness and quality metrics in three study areas for detected trees are 74.00%, 63.50% and 52.10%. The mentioned average metrics for detected low-altitude vegetation cover are 58.00%, 69.40%, 46.30%. The evaluation results indicates that average object-based quality metric for detected trees has highest value with respect to other methods which introduced by other researchers. Also, average pixel-based and object based completeness, correctness and quality metrics for detected trees and low altitude vegetation metrics have acceptable level than other introduced methods.

Given the new approaches of urban management, it is necessary to change the expert-driven, closed, intra-organizational and centralized attitude of urban management to open, participatory, voluntary and citizen-centered management. It is important to note that the smart person is one of the essential requirements of the smart city and in fact the emphasis on information and communication technology and electronic life infrastructures cannot achieve the idea of a smart city without considering the smart citizen. In modern, smart urban management, citizen participation plays a prominent role. Citizen-centric Geographic Information Systems (GIS) as a new concept and approach in urban management provides powerful and effective location-based tools and platforms for real citizen participation in urban affairs. Citizen-centered GIS systems have important applications in various areas of urban management including environmental monitoring and control, crisis management, tourism development, transportation development, refurbishment and refurbishment of worn-out urban textures and so on. Today, we see the use of GIS systems and projects becoming more and more public and citizen-centric. Citizen-centered GIS systems seek concepts and technologies based on the location that enable citizens to participate in management and urban decision making. These systems provide a suite of necessary spatial analysis and analysis tools for citizen participation in identifying and reporting problems in the city, providing solutions to improve urban problems, etc. Before implementing these systems, paying attention to the attitude of citizens and their willingness to use these systems is very important. Therefore, the present study assesses the attitude of citizens towards Citizen-centered GIS and examines their willingness to participate in solving urban problems as well as receiving urban services through these systems. In other words, this study examines the level of citizens' interest in using location-based data and analysis as well as the production of data in citizen-centered GIS. In this regard, District 6 of Tehran as one of the most populated and dynamic areas in the center of Tehran was selected as the study area. The results show that 93.6% of the citizens are willing to participate in reporting urban problems, making urban decisions and urban planning. According to the research findings, 53 percent of citizens believe that media advertising is the best way to cultivate and promote the use of these systems. Among the effective factors in motivating citizens to use these systems, simplicity and attractiveness of the system with 44.6% is the most effective factor in motivating participation and use of these systems. According to the majority of citizens (56.9%), the most important obstacle in using these systems is the lack of trust in municipalities in terms of the effect of citizens' opinions and actions. Citizens’ attitude assessment shows that 93.6% of them agree and strongly agree to participate in reporting urban problems, decision making and urban planning through this systems. The results also show that 70.8% of the citizens tend to interact with different urban organizations. Providing urban services through these systems were also well received by respondents, with 89.6% of citizens agreeing and strongly agreeing to receive many urban services through these systems.

Groundwater is considered the major portion of the world’s freshwater resources. One of the main challenges facing the sustainable development of IRAN is the need for better management of its limited fresh water resources. Hydrogeological mapping of groundwater resources is one of the main tools for the controlled development of groundwater resources. Remotely sensed surface indicators of groundwater provide useful data where practical classical alternatives are not available. Integrated remote sensing and GIS are widely used in groundwater mapping. Locating potential groundwater targets is becoming more convenient, cost effective than invasive methods and efficient with the advent of a number of satellite imagery. The nature of remote sensing-based groundwater exploration is to delineate all possible features connected with localization of groundwater. Data, driven out of remote sensing, support decisions related to sustainable development and groundwater management. With increasing population and urban development as well as agriculture, attention has been paid to the management of surface and subsurface water. One of the ways to manage water resources is to identify water areas with different potentialities and exploit them according to their capacity. Today, due to the efficiency of the GIS, this tool is used to provide a variety of models and zones, at a low cost and time saving, many groundwater issues. All the information layers have been integrated through geographical information systems analysis and the groundwater potential zones have been delineated. Weighted overlay modelling technique was used to develop a groundwater potential model with three weighted and scored parameters. In the present study, a combination of remote sensing data, geographic information system and multi-criteria factors has been developed to prepare a map of susceptible groundwater in the city of Saveh. Thematic maps of each of the factors affecting groundwater, including lithology map, precipitation map, drainage map, land use map, linear Density map, Topographic map, Slope map, Aspect map and temperature map using data Landsat 8 satellite, Digital Elevation Model, geological maps, fractures, soil, land use and rainfall were used. In the next step, after preparing the Raster map these factors, According to hierarchical analysis method, each of them was assigned weight. Finally, the above-mentioned thematic maps were computed using GIS analysis using a weighted algorithm, and a groundwater potential map was obtained. Although the area is characterized by hard rock, the area has been categorized into five distinct zones—excellent, good, fair, poor, and very poor. According to the final map, 14.5% of the area has a very low potential, 7.9% has low potential, 21% has a medium potential, 34.3% has a good potential, 22.5% has a very good potential. The high potential zoning is more consistent with alluvial deposits, plum-pudding stone and coarse alluvial deposits, as well as areas that cover the lands of the garden and the shrub. No potential zone matches to maximum Elevation and other matches with areas that have volcanic rocks and Granite is. Finally, to assess the accuracy and validation of the results, the location of the wells in the study area was used. By comparing the final map and dispersion of piezometer wells, the accuracy of the method used in this study was confirmed. The results of the assessment showed that most wells exist in very good potential areas. Although some of them are also in other areas. This could be due to the fact that in these areas there are many slopes and may have been caused by soil layers in the basement and along faults and fractures that caused water outcrops in those areas. While their power supply is very good at higher potential areas.

Ever-increasing growth and development of urbanization and rapid land-based changes have increased necessity of continuous checking of these changes for urban and environmental planning. Classification of remote sensing high resolution images can be the most effective way to achieve this goal. The classification of these images has always been challenged due to similarities between different classes and differences through one class. Dense classification, also known as semantic segmentation, is also one of the open issues in remote sensing domain. The existence of these kinds of challenges reminds the need for precise methods for classifying images. Deep learning, because of ability to extract deep and powerful features and compatible potential with images, has been known as a good choice in this domain. In this article, in order to cope with the challenges, a convolutional neural networks method based on deep learning is presented for classifying images. The reason for this choice is using deep and comprehensive features by the mentioned method. These features are captured in a supervised manner. In deep learning methods, on the other hand, there is an underlying need for training data and Because of restriction of data in remote sensing, it has been tried to ensure that the number of training samples used in the project is adequate. In this paper, the underlying goal is determination of CNN structure based on deep networks for effective classifying of aerial imagery with high spatial resolution. For this purpose, First, a deep network is designed to extract the deep and optimal features of the aerial image. Architecture and configuration of the deep network are defined in this step. Then, to evaluate the impact of different dimensions of neighborhoods on producing optimal deep features, feature extraction in image patches with different dimensions has been investigated. These patches have been used for train network. After training network with Patches in different sizes, Finally, in order to investigate the classification ability of the deep learning method, in a different approach, a support vector machine has been used for classification based on the deep features produced by the CNN. Comparison of the classification results shows almost same results in the deep learning method in comparison with the conventional support vector machine model, in the same conditions to using deep features. To evaluate the method, aerial data with a spatial resolution of one meter in Des moines area in USA and other data from Royan district in Mazandaran province have been used. Finally, the results of the evaluations show improvement in all three criteria including precision, recall and f1-score in the condition of using larger patches. Also, in general, using of deep learning methods as feature extractor and classifying these deep features by the support vector machine has a bit better evaluation results than feature extraction and classification by CNN.

Despite the capability of remote sensing to direct observation of soil moisture content, the radiances measured by sensors are usually affected by different soil and atmosphere parameters. Therefore, understanding the importance of selecting the optimal features for soil moisture recognition, the application of fuzzy logic to perform intelligent feature selection is a distinguished line of research. In the following, the selected features were used in two widely used classifiers (SVM (Support Vector Machine) and MLP (Multi-Layers Perceptron) artificial neural network) in order to soil moisture classification. These classifiers were found competitive with the best available machine learning algorithms. In other words, the main purpose of this model is to select the least number of features based on fuzzy logic aligning with increasing the accuracy of soil moisture classification. The proposed method was applied and validated using observations carried out for the Iran region. In order to compare the soil moisture classification accuracy using the features selected by fuzzy-based model, a different scenario was also considered. In the latter case, vegetation cover (NDVI), soil surface temperature (LST), and topography as selected features for soil moisture classification, were entered into the above-mentioned classifiers. The reason for choosing these three features among all the features is their significant effect on the amount of soil moisture. The results obtained were very encouraging and indicated about 8% improvement on soil moisture classification accuracy using the proposed feature selection method.

Today, agricultural products have important role in human life. Phenology monitoring of agricultural fields by remote sensing and synthetic aperture radar is useful because it provides key information for farmers with extensive fields. This paper deals with the retrieval of phenological stages of agricultural crops by the degree of polarization (DoP), co-polarized and cross-polarized polarimetric signatures. The DoP is taken as one of the main parameters of the scattered wave, which is received polarization-basis rotation invariant. It is shown that the DoP signature provides information about the phenology that can be complementary to that provided by the conventional polarization signatures. The phenology retrieval is performed by a new approach based on the polarimetric signatures and matching parameters. In this approach, first a signature of each phenological stage is randomly selected as the reference signature. Then, using the matching parameters, the similarity values between the reference and other signatures are calculated. Finally, the phenological stages are identified by analyzing the results. The time series of RADARSAT-2 fine quad-pol images acquired over the Barrax area have been used in this study. This dataset includes a dense revisit time along the growth cycle by combining different incidence angles and different orbit passes (ascending/descending). The experimental results show the good performance of using the DoP signatures, average accuracy 63%, and the similarity between them for retrieving the phenological stages. The DoP signatures are less sensitive to the incidence angle, but more dependent on the physical characteristics of the crops. The results also demonstrate that the matching parameters based on the geometric features of signatures can provide valuable information especially for the oat crop.

Hyperspectral sensors have high capability in identifying objects by acquiring a large number of adjacent electromagnetic bands. Although This large number of bands makes it possible to approximate the more precise spectral curve of the material, it also brings some challenges. The difficulty in data transfer, the weak performance of conventional statistical classifications due to the limited number of training data, and the high processing time are the most important ones. Hence, different methods of dimensionality reduction are proposed for hyperspectral images. In the following article, an unsupervised feature extraction method is proposed based on the bands clustering technique. In the proposed method, after the prior image clustering and forming the prototype space with the aid of the clusters’ averages, the bands are clustered using the K-medoids clustering algorithm. In each cluster, four types of central tendency measures, mean, geometric mean, harmonic mean, and median are used to extract the final features. The experiments are conducted on the three real hyperspectral images with medium and high spatial resolution. Final results indicate that the classification results of the proposed method can reach (72.12) which is 7% higher than the other four competitive methods, principal component analysis (PCA) (64.39), wavelet (64.58), feature selection method based on bands clustering based on variance (65.30) and non-parametric weighted features extraction (NWFE) (64.12) .

With the growing trend towards a world where mobile objects are getting more and more interconnected, location information is increasingly becoming a recognized need for providing rapid and timely information to the social network users. This ability has led the way to an augmentation of existing social network sites with location-based features or the creation of new ones exclusively around geographic information. Within these Location Based Social Networks vast amounts of geographic information are allocated, which attracted the attention of researchers with various scientific backgrounds.One of the hot topics in the field of location-based social networks is mining similarities among users in the terms of location, time and semantic. In this research, we provide a comprehensive review of the methods and criteria used to measure the similarities among the users. We have categorized the existing research areas on this subject and depict a clearer and more suitable perspective for further studies. According to the results of this study, it can be stated that researches in this field have not yet reached a proper maturity and accuracy. In addition some criteria, that applied semantic information and content data, must be studied further in the future.

In recent decades, there have been many problems in urban management and planning due to the population growth, industrial development, urbanization, natural resource depletion and marginalization. On the other hand, with the advent of science and technology, human beings have new solutions to the aformentioned problems. Land use generally means that the use of land in the present situation, that changes over the times. In this regard, the use of satellite imagery, which is an advanced tool for monitoring environmental changes, can help us to investigate these changes. Many studies have been conducted on land use with different approaches and goals, as well as many methods for classifying images and detecting changes in applications. The present research examines various aspects of the dimensions and issues in land use change studies conducted in Iran and reviews the relevant methods. The present research examines various aspects of the dimensions and issues in land use change studies conducted in Iran and reviews the relevant methods. In each of state-of-the-art research, various algorithms and methods have been introduced and implemented that have led to various results and verifications. The correctness of each method is proportional to input data and used algorithms. In other words, we don’t say a method can be considered as the best method in the change detection compairing to other methods.