نشریه علوم و فنون نقشه برداری
سال هشتم شماره 3 (پیاپی 31، زمستان 1397)

In population studies, investigating the spatial distribution pattern of the population is considered as one of the important issues that can be discussed at the level of watersheds. The importance of the distribution pattern of population and human resources in the catchment area comes from the fact that human and population forces interact with each other in their geographical and environmental context, the effects of which, if not in their reasonable logic, will endanger human societies and the environment. The purpose of this research was to investigate the effect of different factors on population distribution patterns and probable vulnerabilities on the population of the watersheds using the capabilities of the spatial information system (GIS). The watershed of Qornuko, located in south of East Azarbaijan province-Iran, was selected as a study area. Based on the research methodology, at first, by reviewing the sources, the most important factors affecting the distribution of population were identified and then data and spatial information related to these factors along with demographic data were collected for the study area. The data used in this research included area map, village's location, topographic map (1/25000), Digital Elevation Model (DEM), rainfall, land use, waterways, roads, faults, landslide and demographic data of 1996, 2006 and 2011 years, which were used to extract required information. Different functions of GIS were used for spatial processing, and statistical processing was done in a spreadsheet environment. Based on the results, over time, the population of mountainous and steep areas decreased while the population of the plain areas was increased. Also, the distance between the villages and waterways has increased, the number of villages and population in the areas with slopes and landslides have reduced, and generally the population and the villages of the area removed from the vulnerable areas. Although the majority of the population in the study area is safe from the dangers of the fault, however, about 36% of the population must be protected against the possible risks of an earthquake resulting from the fault. In general, the process of population changes in the area has mainly been resulted to reduce the threats posed by natural and geomorphologic factors. It seems that there has been a form of compromise between the population of the watershed and its natural factors in order to preserve life and the possibility of utilizing of the study area resources. In addition, it seems that the influence of some factors such as topography and rainfall in the pattern of population distribution in many areas is common and to a large extent predictable, but the situation of the establishment of some villages and population in comparison to some Threat factors (environmental and non-environmental) such as faults, the distance from the streams and landslides, vary from area to area and shows more uncertainty. Therefore, in order to identify and control possible effects on the population due to vital factors such as fault and landslide, etc., the necessity of performing special studies for each watershed is inevitable and should be included in the stabilization watersheds plans. At the end, a vulnerability zoning map for the villages of the watershed areas has been suggested to the interested people in relation to the natural and abnormal factors based on advanced methods such as fuzzy inference system.

The fact of matter is that, aannually, earthquakes create huge losses in life and property to people all around world. On average, about one earthquake with magnitude of  and fifteen earthquakes with magnitude of  occur in the world. So, scientists are always looking for a way to identify the time and place of the event before its occurrence in order to reduce damages and injuries. Earthquake is seismic geophysical phenomena which including irregular, nonlinear and complex processes that's why there is no simple approach to predict its parameters. However, this is a known fact that before large earthquakes occurrence, seismic activity of the Earth can cause abnormal variations in ionospheric parameters. Hence nowadays, study on ionospheric variations has become one of the most important ways to predict earthquakes. Nowadays, as regards development spatial and temporal resolution of TEC data (that is extracted from GPS and other satellites) seismo-ionospheric studies are rapidly improving. In this paper, in order to identify seismo-ionospheric variations, the TEC data has extracted from global ionospheric maps (GIM) associated with the CODE center. The statistical method that has utilised to detect abnormal ionospheric variations is multivariate T2-Hotelling test for two samples. T2-Hotelling test is a very powerful test for detect the unusual changes of mean of two samples. The first sample which TEC reference values has extracted from it,includ data of the 44 to 15 days before the earthquake, then days has removed with high level of solar and geomagnetic activities ( or ). Second sample has formed from four days, finally for investigating this point that whether the difference between the mean of reference sample and second sample’s mean is significant or not, T2-Hotelling test has used. If the test rejectes, it means that difference is abnormal, otherwise it is usual. Abnormal difference may be through the earthquake. In this paper, this method has used for investigating the relation between the ionospheric abnormal variations and earthquakes, for the first time. In order to evaluate the ability of the test to detect seismo-ionospheric variation, 12 earthquakes with the magnitude greater than or equal 6 that occurred in 2010 has studied. Whereas solar activities in the year 2010 were low and several major earthquakes occured in this year, so that this year has selected for study. Given that the behavior of the ionosphere at diverse latitudes is very different, earthquakes have chosen with various latitudes.Also earthquakes have selected from areas with good IPP coverage. According to the results, this statistical test can detect seismo-ionospheric anomaly for 9 cases of studied earthquakes. These anomalies have generally observed around the center of earthquakes and these are often identifiable a week before the earthquakes (for Haiti, Drake, Honshu, Mexico and Ecuador earthquakes). Another important point is that anomaly of Ecuador earthquake, despite its great depth (206.7 km) is well identified. Results generally indicate that T2-Hoteling test at confidence level of 99%, has been more effective in this study.

According to (Singh 1989), Change detection ( CD ) is “the process of identifying differences in the state of an object or phenomenon by observing it at different times”. Change detection from remote sensing images is very important in Geomatics’ applications such as urban and rural management, environmental applications, forest monitoring, agriculture, and so on. There are some essential processing tasks including geometric and radiometric corrections, feature extraction and selection, and classification, which must be performed on multi-temporal images, to do change detection. Radiometric correction is one of the most important tasks in change detection, since atmosphere condition, seasonal effects, and the change on view angle of sensors, and so on make a difference on illuminations of a pixel in two dates. There are many errors in the outcome of change detection without applying radiometric correction task. For this reason, a radiometric correction approach was developed in this study to do an appropriate change detection method. There are some radiometric correction approaches, which works based corresponding pixels. They use linear regression, neural network, and so on to make a relation among digital values of images in time series images. Undoubtedly, the corresponding pixels play a key role in radiometric correction approaches to solve unknown parameters of linear regression and neural network. Then, we proposed an efficient approach to select proper corresponding pixels. In this research, three radiometric correction approaches including Pseudo Invariant Features ( PIF ), Radiometric control sets method ( RCSM ), and linear regression along with a new radiometric correction approach were implemented. To evaluate radiometric correction approaches, two case studies including Lake Urmia and Alaska were chosen. Landsat TM satellite images were also obtained from the study areas. Results show that the accuracy of change detection outcomes depends on threshold values of radiometric correction approaches. Moreover, overall accuracies of linear regression, pseudo invariant features, radiometric control sets method, and our proposed approach in the first and second study areas were (68 % , 93 % , 91 % , 94 % ) and (92 % , 95 % , 97 % , 97 % ), respectively. It indicates that our proposed radiometric correction approach is more efficient than linear regression, Pseudo Invariant Features, and radiometric control sets method. In the future woks, it is recommended to use non-linear functions instead of linear one in our proposed approach to achieve more accurate results.

Introduction Persistent Scatterer Interferometry (PSI) is a technique to detect and analysis of a network of coherent pixels referred as Permanent/Persistent scatterer (PS) which are stable throughout time-series of SAR images. This technique has been applied to monitor and measure phenomena such as earth subsidence fault movements and earthquake volcanic activity and other geological and environmental studies. In all PSI techniques, the processing is carried out only on the PS pixels. Therefore, high density and phase quality of these pixels are the most effective factors on the results of these techniques. The main challenge of this technique is to detect the coherent pixels over non-urban areas which suffer from the temporal decorrelation. Nowdays and with the development of polarimetric SAR sensors, polarimetric radar data are available. Polarimetric data consist of several conventional SAR acquisitions, usually addressed as channels. Each channel in a PolSAR acquisition is sensitive to different geometric characteristics of the scene. This additional redundancy over the scene may allow to increase both quality and density of the PS pixels. Therefore, the combination of polarimetry and interferometry enables to improve the effectiveness of PSI techniques, especially in non-urban areas. In this paper, we employ a method to improve the conventional PSInSAR algorithm for detecting PSC by using polarimetric optimization method on dual-pol SAR data. The improvement of this research is based on minimizing ADI criterion by means of an Exhaustive Search Polarimetric Optimization method to increase the number of PSCs. Materials & Methods 2.1 Dataset Description   The proposed method is tested using a dataset of 17 dual-pol SAR data (VV/VH) acquired by Sentinel1-A satellite March 2017 and October 2017.    2.2 Polarimetric SAR Interferometry   A general formulation for polarimetric SAR interferometry was proposed in (Cloude & Papathanassiou, 1997). The scattering matrix S represents the polarimetric information associated to each pixel of the scene. Considering a monostatic configuration, the scattering matrix S is defined as follows:   (1) where , are copolar terms,  is the cross polar term. This can be represented with the target scattering vector  using the Pauli basis as: (2) where  is the transpose operator. In Sentinel-1configuration (VV/VH), where there is no knowledge of  , scattering vector  can be written as: (3) In order to generate an interferogram, each target vector  can be projected onto a unitary complex vector  . Result of this step obtains the scattering coefficient μ defined as: (4) where i corresponds to two images, and * represents the conjugate operator. The scattering coefficient μ is a new channel or polarization state which is a linear combination of the Pauli vector elements. In this regard, all interferometry techniques can be extended from single-pol configuration to a desired polarization state by using (4) and (5). The projection vector for dual-pol data defined as:   (5) where  and  are two real parameters whose ranges are finite and known and are related to the geometrical and electromagnetic properties of the targets. The parameter  represents the type of scattering mechanism and  corresponds to orientation of scattering. In our research, the main purpose of polarimetric optimization is to search in a two-dimensional space,  and , to find an optimum projection vector, .   2.3 Amplitude Dispersion Index Optimization   In order to generate a polarimetric form of , it is sufficient to replace scattering coefficient, , in (6) by  as define in (4):       (6)       (7)   The main issue in the ADI optimization is finding a projection vector  for each pixel, which leads to minimize the  value. Results & Discussion Our results confirm that the algorithm substantially improves the PSInSAR performance, increasing the number of PS pixels with respect to standard PSI, and increasing the phase quality of selected pixels. The results reveal that using the optimum scattering mechanism increases the number of PSC about 2.6 times and PS density about 2 times than using single channel datasets. Also, the effectiveness of the method is evaluated in urban and non-urban regions. The experimental results showed that the method was successful to increase the final set of PS pixels in both regions significantly. Conclusion In summary, it can be inferred that the polarimetric optimization method is successful to increase the number of the final set of PS pixels in different regions, significantly.

Images material identification plays an important role in remotely sensed image processing and its applications. Hyperspectral images, which contain a lot of narrow spectral bands of the electromagnetic spectrum, have a great potential for information extraction from remotely sensed images and material identification. Due to the low spatial resolution of hyperspectral cameras, material mixing, and light multi scattering, these images usually contain a lot of mixed pixels which face material identification with many problems. Recently, hyperspectral unmixing methods have been widely considered by the researchers as a powerful tool for identifying materials in the mixed pixels. Some of the algorithms, proposed for hyperspectral unmixing, are based on the linear mixture model (LMM) and the others are based on nonlinear mixture model (NLMM). LMM-based algorithms are more simple and commonly used methods for hyperspectral unmixing. Among various algorithms, based on LMM, non-negative matrix factorization (NMF) has attracted the most attention due to essentially implying non-negativity of the endmembers and their corresponding abundances, and moreover, simultaneously extracting spectral signature and abundances of the endmembers. In spite of these capabilities, NMF leads to local minima due to its non-convex objective function. In this regards, various studies have attempted to lead NMF results to the global optimum by imposing some additional constraint to the main objective function of NMF. However, NMF-based methods still suffer from the problem of falling into local minima. To tackle this problem, an iterative post-processing procedure, based on an ensemble learning technique, has been presented in this paper.  The main goal of this paper is to demonstrate the ability of ensemble learning to improve the hyperspectral unmixing results in a simple and non-parametric manner. To this end, NMF with  sparse constraint is performed in several iterations, and then, the results of each of these iterations are weightened on the basis of identifying a primary endmember that certainly exists in the image. Weightening is done with calculating spectral angle distance (SAD) metric between the true and extracted spectral signatures of the primary endmember. Usually, there is prior information about the hyperspectral images such as some existed materials or the number of materials in the images. Therefore, it is always possible to find a primary endmember in an image. The accuracy of identifying primary endmember is extended the accuracy of identifying other endmembers of their corresponding abundances. Final mixing and abundances matrices are determined using weighted combinations of the mixing and abundances matrices, extracted from each of the iterations. The proposed procedure is nonparametric and mathematically clear which can be extended to more advanced algorithms of hyperspectral unmixing. The performance of the proposed method for extraction of endmembers and their corresponding abundances has been evaluated using various synthetic and real hyperspectral data sets. Synthetic hyperspectral images constructed using USGS spectral library with several numbers of endmembers in the different signal to noise ratio (SNR) levels. Cuprite hyperspectral image, acquired by the AVIRIS sensor in 1997 from the Nevada Desert in the United States, has been used in this study as a real hyperspectral data set. The results of the experiments on both types of hyperspectral data illustrate the superiority of the proposed method over state-of-the-art competing methods.

High spectral resolution of hyperspectral images in form of very narrow and constant within visible and infrared spectral ranges has brought the technology of remote hyperspectral measurement into spotlight in order to detect objects as well as earthly phenomena. In this field, most methods have been presented with the purpose of improving the accuracy of image classification and there have been scarce researches on target detection (TD). Supervised  TD  problem  can  be  considered  as  a  one-class classification problem between the target and non-target pixels using  training  data  from  the  target  class  only.  However, a spectral signature of the target sample obtained using field or laboratory measurements is the only training data available for TD. A substantial number of bands lead to heavy computational costs along with Hughes Effect on hyperspectral image processing. Hence, in recent years much attention has been paid to reduction of computational complexity in the processing of hyperspectral images. In comparison to the classification  field,  few  studies  have  been  done  on  dimension  reduction  or  band  selection  for  target  detection  in  hyperspectral images. A chief reason behind this is the shortage or absence of training samples of the desired target in background images. In order to solve this problem, a method is introduced based on target simulation. Recently, target simulation method has been used for creating artificial sub-pixel targets on hyperspectral images in order to investigate the performance of sub-pixel target detection (STD) algorithms. But in this paper target simulation has been used as method for optimum band selection. In this method for STD several simulated training samples, created by means of target spectrum implantation in the image. For optimal band selection after achieving sufficient implanted targets as training data, searching strategy in hyperspectral image space is of high account. Once simulated targets are created, optimal bands are selected via Particle Swarm Optimization (PSO) Algorithm. To make the optimization algorithms exploit well in search space, their cost functions must be well defined. So one of contributions of this study is defining a new cost function for optimization algorithms used for selecting optimal bands. In the next stage, based on the optimal bands selected, the Local ACE is applied on the image to obtain detection result. ACE algorithm, one of the most useful and common algorithms for detection of sub-pixel and full pixel targets. But local ACE is commonly used to detect sub-pixel targets in hyperspectral images. In this version of the ACE algorithm, instead of using the mean and covariance of the entire image, just the neighbouring window pixels are used. The output of this stage will be TD map that by applying a threshold in a detection map can determine whether the pixels are target or not. In order to evaluate and study the ability of the introduced algorithm, Target Detection Blind Test (TDBT) of Hymap dataset and Hyperion dataset from Botswana have been used. False alarm rate was used as touchstone to evaluate the results between the outputs of different methods. Compared to some PSO-based algorithms such as maximum-submaximum-ratio (MSR) and correlation coefficient (CC), an evolutionary method such as genetic algorithm (GA), and the use of full band, the proposed method was able to improve the results by 46% in all cases where it was possible to decrease false alarm for the searched target.

Analysis of movement data is a new trend of research in GIScience. Recent and emerging positioning technologies and wearable sensors such as smart watches have led to increases in the availability of various kinds of data for moving objects. Thus, new exploratory tools and knowledge discovery techniques are required to extract meaningful information, discover interesting patterns, and explore the dynamic behaviour of moving objects in order to transform raw trajectory data into useful information to be used in reality interpretation and decision making. Especially, analysis of movement observations, which contain information about the movement of each individual entity and the underlying mechanisms, are of great interest. These observations are key to the study and understanding of movement behaviour. It is essential for movement behaviour studies to take into consideration the so-called movement parameters (MPs), e.g. speed, acceleration, or direction, which are key to characterize the movement of objects. For example, in some sports, it is important to be aware of the heart rate or speed patterns of athletes through running or walking at different times of the day in order to detect the athletes’ performance. In such cases, analyzing a persons’ movement observations in terms of space (x, y, z) and time (t), through considering movement attributes of each person and contextual information (e.g. the environmental information such as temperature) will allow a better understanding of behavioural movement patterns and effect of different parameters on patterns as well. This article proposes analysing walking observations to extract behavioural pattern of attributes (such as speed and heart rate) of a person to examine the influence of different conditions on behavioural movement patterns. For this, a segmentation approach is proposed to project the “movement parameter profiles” into a pattern for analyzing changes in different movement attributes of each individual during walking in order to identify the effect of each personal context (e.g. gender of the person) and environmental context (e.g. day time, tiredness, weather condition, etc.) on the movement patterns. A measure to assign distance value is then used to examine the effect of contexts on the movement. As expected, the results show that time series of each attribute for each person have a unique pattern describing the persons’ movement behaviour, which changes in different contexts. For detecting the effect of different personal and environmental conditons on the behavioral movement patterns of people during walking, the comparing procedure was implemented on movement parameter patterns with the same conditions and different conditions respectively and a running index was introduced to model the similarity and persons performance during walking. This model was used to predict the similarity between the movement parameter patterns with different conditions. Finally, by comparing the predicted value and the similarity which calculated by proposed approach, we could reach the effect of different situation on behavioral movement pattern of people during walking.

Obtaining reliable estimation and information about particulate matters (PM) with aerodynamic diameter less than 10 micron is crucial. This is due to the PM10 serious negative impact on human health and environment. Therefore, studying the concentration and spatial pattern of the PM10 in urban cities, especially populated and industrial cities, has become a popular an important subject for researchers. For this purpose, several pollution stations have been established in different part of cities. These stations have the capability of measuring and recording the concentration amount of various pollutant including PM10, PM2.5, and etc. however, pollution stations can measure the concentration amount of various stations precisely, their measurements are not spatially connected and only spars point observations are provided. One of mostly used and efficient methods to solve this problem is to use remote sensing data. Aerosol optical depth (AOD) and aerosol contribution to apparent reflectance (ACR) are two mostly used remote sensing data which have been used to study the concentration and spatial pattern of PM10. In this study, we have adopted to use ACR images instead of AOD based on three reasons including their higher spatial resolution, spatially connected (without any gap), and at last the absence of aerosol robotic network (AERONET) for AOD retrieved values evaluation in our study area. ACR images can be generated though a relation using red and SWIR (2.1 µm) bands. In particular, we can estimate the surface reflectance (SR) of the Red band from top of atmosphere reflectance (TOAR) of the SWIR (2.1 µm) band. This is possible based on an assumption that the aerosol’s effect on SWIR band is negligible because of its higher wavelength. After the estimation of Red band SR, the difference between Red band SR and TOAR can be an illustration of the amount of atmospheric reflectance. In this study, moderate resolution imaging spectroradiometer (MODIS) level-1B images identified as MOD02HKM with 500 m spatial resolution for 12 days over Tehran, Iran have been utilized. Their corresponding ground measurements of PM10 concentration from 13 pollution stations spread have been used. We have used artificial neural networks to develop the model to estimate the PM10 concentration from ACR data. This has been done in two different approaches including daily modeling (each day separately) and overall modeling (using 12 days data together). Artificial neural networks with one, two, and three hidden layers and sigmoid transfer function using the levenberg-marquardt algorithm are employed to model the relation between ACR values and corresponding PM10 concentration. Also, pollution maps are generated to investigate the spatial pattern of the PM10. In daily modeling artificial neural networks with one, two, and three layer(s) achieved 0.769, 0.806, and 0.848 R2 values respectively, representing the higher capabilities of artificial neural networks with more hidden layer. However, it should be noted that the increase in the number of hidden layer will result in overfitting of the model to the training part of data. Finally, the overall modeling using artificial neural networks with one, two, and three hidden layer(s) obtained 0.412, 0.499, 0.503 R2 values.

Cutaneous Leishmaniasis (CL) is an endemic, parasitic and a vector-borne human disease that is transmitted by the bites of female sand fly. The little attention has been paid to control it, which has imposed an economic burden on societies and has created a public health issue in the Middle East and worldwide. An approximated 12 million humans are infected, and about 1.5 to 2 million new cases of CL happen each year worldwide. More than two-thirds of new CL cases currently happen in six countries: Iran, Colombia, Algeria, Syria, Brazil and Afghanistan. The spread of the CL is highly associated with the spatial parameters, spatial and socio-ecological interactions of vectors (sand fly), hosts (human), reservoirs (rodents) and the environment. The heterogeneity of these interactions has hindered the accurate modelling of the CL for healthcare preventive measures in endemic areas. Agent-Based Modeling (ABM) can be used as a new technology to study these behaviors and to simulate the spread of the disease and complex interactions. The capabilities of geospatial information systems (GIS) can also be used to facilitate the collection, analysis and display of disease information. Therefore, the contribution of this paper is the integration of GIS, ABM and an improved Susceptible-Exposed-Infected-Recovered (SEIR) epidemic model for exploring the CL spread. To evaluate the proposed model, data from the Maraveh-Tappeh County in Golestan Province, an endemic area in Iran, including CL notification, landscape and census data, were used. The physical and social environment of northeastern Golestan has been mainly characterized by the presence of different factors that have been recognized as strongly associated with CL, large rural populations, suitable climatic conditions, riverbeds, desert and lowland area. Using various simulations and scenarios in this study, indicated how the CL spreads in different parts of the study area with various socio-ecological conditions. The results of this model indicated that the spread of the CL occurs principally in arid and low altitude areas, riverside and population centers. The results also showed that the restriction on human activities in rural areas leads to reduce the severity of the epidemic. The results also showed that the spread of the CL has a particular pattern, and the prevalent cases of the CL occur in the second half of the year. This finding could be due to the latent period of this disease and inactivity of sand flies in the cold seasons of the year. As a result, preventive behaviors can be implemented in order to control this disease by the obtained patterns and achieve the effects of these behaviors. Moreover, the impact of preventive measures on reducing and controlling of the CL was shown by the visual presentation of the propagation of this disease through the different scenarios, including population size, latent period, infection period, sensory range of sand fly's bite, insecticides, bed nets, health services and rodents control. Finally, the outbreak of the CL has a spatiotemporal pattern in the case study which is related to a complex cycle where the sand fly, rodent, human and the environment have a significant influence. This modeling effort can lead to a better understanding of disease spread dynamics and eventually to take better steps towards the prevention and control of CL spread.

Movement as “a change in the spatial location of the whole individual in time” is the result of complex states and behaviors of moving entities or processes. “Movement data link together space, time, and objects positioned in space and time. They hold valuable information about moving objects, properties of space and time as well as events and processes occurring in space and time”. Individuals’ movements are measurable responses to the combination of internal states, physiological constraints, and environmental parameters, which control most of the essential behaviors of that entity or phenomena. Therefore, it could be the base for analyzing the behavior of entities or movement phenomena in spatio-temporal spaces. The importance of studying the behavior of people and the expansion of access to spatial data has led to development of activities related to study of movement of individuals as well as discovery of patterns and behavior of individuals for better to use in urban planning and policymaking. Understanding the relationship between demographic and temporal variables and human movement as well as extraction of behavioral patterns is essential assess different social issues. for example, understanding the way that people move, amount of their movement, and their internal interactions can lead to better understanding of different social issues such as spreading a special disease, locating infrastructures and city management, reducing traffic, and structure of urban communities. In this research, activity space is used to extract the relation between demographic and temporal variables and mobility behavior of people. It is simply the environment or area within which the user moves so it indicates dispersion of the places that user visits. It actually constructs a structure for the places visited by the user. Investigating the activity space helps to infer characteristics of the users’ movement and understand the difference between their mobility behaviors. It is a frequently used measure “that captures individual and environmental differences and offers an alternative approach to study the spatial reach of travelers”. Among three available types of activity space (i.e. standard deviation ellipse, minimum convex polygon and daily path area), the standard deviation ellipse is used because it focuses on general shape and direction of the activity space “without introducing potential error introduced by using geographically distant points”. Determinants of the activity space (such as area, shape index, radius, entropy and ratio) represent the characteristics of individual’s movement behavior. This research aims to explore a Swiss human movement sample dataset, called MDC, in order to discover the effect of demographic and temporal variables on human movement patterns in Switzerland. Data analysis and comparison of results indicate that age, working and time are decisive demographic and temporal variables for area and shape index of the activity space so they are useful for understanding some of the human’s movement characteristics. The results declare that middle age users, females and people who work have more active mobility pattern, since they have higher area and shape index than users in other groups. Moreover, analyzing the standard deviation of area and shape index of the activity spaces for time groups indicates that people tend to travel further in weekends and summer than weekdays and winter.

Many users around the world use the services of pedestrian/vehicle navigation systems. Due to the design and construction of large and complex structures, wayfinding in buildings is also very important. Indoor spaces elements (such as rooms, corridors, walls, doors, elevators, etc.) are different from the outdoor spaces elements, so the first step of indoor navigating is extracting the navigation graph of these spaces. The graph obtained from the interior spaces has various criteria because of the variety of factors affecting the pathfinding of individuals. Therefore, the collection and assignment of these criteria to the navigation graph are necessary which based on the characteristics and interests of each user. The aim of this study is to extract the navigation graph of the historical covered bazaar of Tabriz city and routing in it. The UNESCO recognizes Tabriz Bazaar as world heritage site in 2010. This bazaar has 20 rastes, 35 saras, 25 timches, 11 dalans, 9 religion schools, 8000 shops, and 40 types of jobs. Some places such as mosque and hoseyniyes are special places for Iranian bazaars, which are places for traditional and religious ceremonies. This Bazaar was located in the central part of the Tabriz city and has linear structure. In other words, the present study addresses two important issues in the field of indoor navigation. The first issue is the production of indoor navigation graphs for one of the most complex indoor space in the world, the historical covered bazaar of Tabriz city, based on IndoorGML standard principles. The second issue is the collection of criteria for this graph in order to navigate disabled people and routing them. IndoorGML is an OGC standard for an open data model and XML schema for indoor spatial information. It aims to provide a common framework of representation and exchange of indoor spatial information. The goal of this standard is therefore to define a framework of indoor spatial information to locate stationary or mobile features in indoor space and to provide spatial information services referring their positions in indoor space, instead of representing building architectural components. IndoorGML is intended to provide the following functions; 1) Representing the properties of indoor space, and 2) Providing spatial reference of features in indoor space. Connecting indoor and outdoor spaces is an important requirement of indoor spatial information. IndoorGML provides the concept of anchor node to connect indoor and outdoor spaces. Every indoor space contains at least one entrance, and it can be used to connect indoor and outdoor spaces. In IndoorGML, entrance is represented as a special node of topological graph in indoor space. We call it anchor node, which differs from other node in topological graph, since it may include additional information for converting indoor CRS (Coordinate Reference Systems) to outdoor absolute CRS. The dual graph from the Tabriz bazaar’s plan was drawn then the obtained edges summarized and connectivity graph created. For Tabriz bazaar’s graph, 360 main edges with a length of 9226.77 meters and 312 nodes were extracted. Five criteria including length, width, slope, obstacle, and stair were collected for each edge thought field observations and were stored in the attribute table. Finally, wayfinding was assessed and was compared for ordinary users and wheelchair users. The results indicate that 43% of the edges are unsuitable and 7% of main segments of the bazaar are Inaccessible for wheelchairs.

In the past decades, Differential interferometric synthetic aperture radar (DInSAR) techniques have been employed to estimate the Earth’s surface deformation with high resolution. All pixels of monitored area are not appropriate to use by classical DInSAR techniques. The quantity of reliable pixels in the studied area and their phase quality are important factors that can limit the efficiency of DInSAR techniques. In order to avoid decorrelation influence and other limiting factors in DInSAR processing, we need to identify reliable pixels to estimate deformation rate. Permanent scatterer interferometry (PSI) technique has been extended the capability of traditional DInSAR methods as they process pixels that have partial decorrelations. These approaches employ pixels which are stable during the data acquisition. These pixels are referred as persistent scatterer (PS). In literature, PSI techniques applied on SAR data images that are usually acquired by the sensors providing single- channel data. Polarimetric capability of current satellite sensors, such as Radarsat 2, ALOS-PALSAR, TerraSAR-X, and UAVSAR, makes it possible to have SAR data with more than one channel. In this paper, we present an approach for increasing the quantity of permanent pixels. These pixels are selected for processing in DInSAR using polarimetric information prepared by new sensors. The objective of this paper is to investigate existing algorithms that confirm the contribution of polarimetric data in order to  improve persistent scatters (PS) detection. These algorithms are formulated based on two different selection criteria: amplitude dispersion index (ADI) and mean coherence. High average coherence or low ADI quantity is associated to the PS candidates and better phase quality that make deformation maps more reliable. Different approaches such as BEST, MIPO and ESM_MB are analyzed to optimize both selection criteria in terms of pixels’ quantity and density and their results are compared. Experimental results with exploiting a stack of Uninhabited Arial Vehicle Synthetic Aperture Radar (UAVSAR) quad-pol data sets over an urban area in CA, provide the expected improvement. For two criteria, the so-called BEST is the simplest method with proper computational cost time. In the MIPO approach, the polarimetric channel that maximizes the mean intensity of data over time is achieved based on eigen problem. This is one of the estimators applied over the intensity optimized data to select PSs. The ESM-MB approach provides larger improvement in both estimators. In the coherence estimator, due to computation load of the numerical solution, we tested analytical solution to find the optimum projection vector for each pixel. In ADI, due to lack of an analytical approach, we applied a numerical optimization technique for optimizing the projection vector in the ESM-MB method. Comparing the number of PSs based on quad-pol with single-pol data illustrate remarkable improvement in both selection criteria (average coherence and amplitude dispersion index). For quad-pol case, we achieve an increase of 60% with respect to single-pol data when using average coherence and over 6 times more for amplitude dispersion index. In fact, increasing the density and quality of PS, could provide deformation maps with high accuracy. The results of this paper confirm the suitability of polarimetric data in order to improve the performance of PS detection in monitored areas.

Every year, natural disasters have devastated many victims around the world. The earthquake is one of the above natural phenomena that Iran is vulnerable to earthquake events due to being seated in earthquake zones of the world and possessing a relatively high population. Various factors affect the damage caused by earthquakes. Among these factors, we can indicate the time and severity of the earthquake, the strength of the structures, the type of use of the structures and the way of relief after the earthquake. Therefore, studying in these areas, and in particular, the promotion of earthquake relief and rescue, can have a positive effect on the reduction of human and financial losses. In this regard, during the present research, the duties and activities of the rescue and rescue forces were investigated at the time of the earthquake. Also, due to the impossibility of placing in post-earthquake conditions at the time of conducting the present research, an assumed earthquake in a similar study area The amount of damages to the region has been calculated. Considering the damage inflicted on the study area, the present research determines the priorities of the relief and rescue activities, and then the relief and rescue forces will be allocated to the selected activities with consideration of their abilities and specialties. To carry out the research, simulation of relief and rescue operations is considered for a group of operations with six rescue teams of eight. Also, due to the objective of this research, which is the optimal allocation of relief and rescue teams to the area of ​​earthquake occurrence, for the optimal allocation of relief and rescue forces to the activities of the region, the spatial information system and the tabu search algorithm have been used and similar The aforementioned tools such as earthquake events and their damage estimation, rescue teams, and descriptive information are introduced into this system. Also, a function under the title of income has been introduced in this study, which serves as a target function in the tabu search algorithm. It is considered and optimized. Finally, the results from the implementation of the proposed algorithm in this study have shown that the use of the tabu search algorithm to optimize allocation of relief and rescue teams after an earthquake, then when this algorithm is not used to optimize the target function, and Also, earthquake simulation, teams and activities are not performed according to the model presented in this study, improvement of 1.41 fold. Given the fact that the structure of relief and rescue teams has been done according to reality, the use of this algorithm to optimize the simulations of this research as well as the implementation of the scientific and practical structure of the activities and rescue teams will be a modern and efficient way to improve the quality of post-earthquake relief and rescue.

Detecting agricultural land borders from high-resolution imagery is a major challenge due to the complex earth environment, which has become a significant issue in researches. Lack of location data to determine the area and location of agricultural land, to control and plan about cultivation and food, as well as to estimate the statistical parameters of agriculture for optimal management in agriculture, etc. shows the necessity of extract the boundaries of these lands. For this purpose, active contour models are a powerful tools for detecting and tracking image objects. In order to develop these models, the combination of two nonparametric region based Chan-Vese active contour and edge based Geodesic Active Contour models was considered to simultaneously apply edge and region information in a hybrid math structure to extract the agricultural land boundary. This combination is able to separate just one type of object from background; Therefore to increase the efficiency of the hybrid model and its development for color images, the hybrid model was combined with the Multiphase Active Contour model. After evaluating the proposed model with the reference produced map, the resulting values for the evaluation criteria were 90.3 for the overall accuracy index, 86.1 for the Kappa coefficient, 85.5 for the product's accuracy index, 87.7 for the user's accuracy index and 81.9 for the quality index, which were obtained from four images with different characteristics. After comparing the values obtained for the desired indices, the proposed model showed an average improvement of 9% for the CAPA index, 6% for the overall accuracy index, 12% for the product's accuracy index, 14% for the user's accuracy index and 12% for the quality index, increasing the accuracy rather than the basic model.

One of the most well-known methods used by the public to share spatial knowledge is the sketch maps. These types of maps can be considered as an effective way to collect and share spatial data by depicting some of the spatial cognition of individuals from the real world. Because of the difference in the cognition of individuals from the environment, each of the sketch maps drawn from a specific region can include different information. An important question to be asked here is how the potential of these maps can be benefited more and better in order to collect spatial data in spite of these differences. One of the proposed methods for this purpose is to combine and integrate information extracted from different sketch maps of a region with the aim of obtaining a map with more complete information than any of the original sketches. Although this integration can provide a better use of the potential of the sketch maps for collecting spatial data, prior to that, it is necessary to consider the matching process between the features in the sketch maps. However, there are several types of features in sketch maps, but all sketches contain pathways and these pathways can be identified by name, or based on specific places or Points of Interest (POI) located in the proximity of them. Accordingly, in this paper, a new solution is proposed for matching between the existing pathways in the sketch maps. In order to describe each pathway in the sketch maps, in addition to the name of the pathway, we can use information that can be extracted indirectly from these sketch maps. To describe a specific address, even if we do not know the correct names of the pathways, we can use the names of near POIs or other related pathways. The same point is used in this paper to describe the pathways in the sketch Maps. Accordingly, the matching parameters are considered as two main parts. These parts are the proximity of POIs and intersections of pathways with matched well-known pathways. Therefore, in the first step of implementation, based on the descriptive information as well as the relative distance between the POIs, the clustering process is performed on POIs location. Subsequently, based on the qualitative spatial relations between clusters and pathways, descriptive information of the pathways, the arrangement of the pathways around the intersection points, pathway intersection with roundabouts and matched well-known pathways, the process of matching of the four sketch maps is done. In order to evaluate the accuracy of this method, the outputs of the matching process are compared with the manually matching, and then, the precision and recall values ​​for each matching are calculated. The average values ​​of these two parameters (89.63% for precision and 87.1% for recall) indicate that this solution has an acceptable accuracy compared to other studies in this field and therefore, the proposed solution can be exploited in future works as the basis for the integration to enrich the existing  metric maps with less details.

Cities they have not been safe from damage to natural and man-made disasters throughout history. One of the issues that most people face, especially the human settlements in the world's major cities, is the issue of natural hazards. An earthquake generates many financial and financial losses everywhere in the world. Earthquake damage to life and property every year creates a lot in different parts of the world. Since Iran is considered as the world's most seismic region, the need to confront this phenomenon is strongly felt. One of the secondary effects of the earthquake is liquefaction. Liquefaction caused serious damage to many structures especially buildings and residential areas. In addition to the low quality of buildings, Tehran's location on important faults and the presence of earthquake-induced liquefaction phenomena due to high water levels in some parts of southern Tehran have increased the vulnerability of urban areas against earthquakes. Most of Tehran's soils are alluvial and sandy soils due to lack of sewage channels and rising water levels, especially in the southern part of the study area, forms a loose, sandy and saturated water layer due to the earthquake of this layer. The earthquake of this layer is dumped and tilted into a building that is not damaged even by the earthquake's horizontal force. Yasuda and Hashimoto discussed the damage caused by liquefication in residential areas during the Great Japanese earthquake in 2011 and reviewed the pattern of land improvement in damaged areas and the effects of reducing groundwater levels in damaging residential properties. In this study, we have tried to consider, in addition to considering the parameters that affect vulnerability (ground and underground factors), The groundwater surface parameter and its effects on the liquefication and vulnerability of the buildings are examined. The main objective of this study is to prepare a map of seismic vulnerability of urban buildings in three areas of Tehran and to study the effect of groundwater level using two groups of physical parameters (with underground and without underground factors) and human parameters. Methodology The study area in this study is 11, 16 and 20 municipalities of Tehran. These areas have been selected since the height of water varies between the northern and southern regions. In order to prepare a seismic vulnerability map emphasizing the level of groundwater aquifers, at first, three different types of building vulnerability criteria were extracted using library and documentary studies and experts' opinions. Indicators were determined and collected, then data were prepared and a number of criteria were prepared based on the type of indicators (land, underground, human) 15 output map provided. Then to apply the mathematical model of the base set for each of the three sections, separate rules were written. In general, 25 laws have been extracted and the rules have been correctly segregated. After applying the rules and combining the data used in accordance with the correct rules extracted, 3 output maps were obtained. The compilation of these maps by Rough method provides a final map of the physical seismic vulnerability of three districts in Tehran. In this research, in order to investigate the uncertainty of the theory of rough collections (Rough Set Theory) in determining the seismic zoning of urban buildings in three areas of Tehran, taking into account human factors, ground and underground factors with emphasis on groundwater level and thus, the seismic vulnerability of urban buildings is modelling. Results of its implementation and analysis One of the goals of this research is to reduce uncertainty in parameters, which causes uncertainty in the issue of determining seismic vulnerability. The output of seismic vulnerability maps shows that although the vulnerability map is based on the Rey fault active scenario, However, considering the underground factors in estimating the vulnerability of the parts of the north and part of the southwest of the region are highly vulnerable. This is due to the geology and soil of the studied area, including old river alluvial and dense sand. Also, from the north to the south of the region, the depth of the groundwater level decreases, which makes it possible to transfer the vulnerability from north to south and to a large part of the center, south, and south-east of the study area by adding the groundwater depth parameter. Conclusion In this paper, considering the underground factors and the effect of the parameters of groundwater depth, along with other parameters of the vulnerability level in three areas of Tehran, was estimated. The results of Physical vulnerability without underground water indicate that 40% of buildings have a very low vulnerability, 26% of buildings have moderate vulnerability and only 10% of the buildings are a high vulnerability. In the event that results of the physical vulnerability to underground water show that only 2% of the buildings have a very low vulnerability, 15% of the moderate vulnerability and 60% of the buildings are a high vulnerability. By adding the parameter, the depth of the surface of groundwater vulnerability in the large part of the center and south of the study area has expanded and has increased significantly in all domains. After implementation of the Rough Model, 25 correct rules were extracted from the available information, and a seismic vulnerability map was developed in three areas of Tehran. The results of the region's final vulnerability indicate that 6 percent of urban buildings encounter very low vulnerability, with more metal skeletons, 20% of the buildings in the area are low vulnerability, moderate vulnerability group is 39% of the buildings, 19.9% of the buildings in the area are high vulnerability and 13.8% very high vulnerability.