نشریه علوم و فنون نقشه برداری
سال یازدهم شماره 2 (پیاپی 42، پاییز 1400)

Gazetteers are geospatial dictionaries of geographic names containing triples of place names, geographic footprints, and feature types for named geographic places. As an important element in Geospatial Information Retrieval (GIR), these precious resources should be enriched according to new applications. . Identification and adding new place names to the gazetteer, and keeping it up to date are important issues in the gazetteer enrichment. The main challenge in this era is that in most gazetteers only a top-down approach is considered. Consequently, most local place names are ignored in such gazetteers. In addition, updating gazetteers is a time-consuming and expensive process. Since the emergence of Web 2.0, using volunteered Geographic Information (VGI) and social media in harvesting place names have been attracted the attention of many researchers due to containing local place names and recently created ones. In a similar condition, online property ads published by people contain such place names. This article presents a data-driven method for identifying urban place names including neighborhoods and main streets using online real estate advertisements.

The online real estate ads of four metropolises including Tehran, Mashhad, Isfahan, and Shiraz mined from the Divar website. After n-gram extraction and applying required pre-processes, the n-grams got labeled. To remove outlier points from an n-gram set and consider the scenario that several places can have the same name through a city, the point set of the n-gram get clustered. Based on a set of spatial statistics, the random forest models on housing data of each city trained and then tested on the ads data of other cities.

The results show that either in detecting the main street or neighborhood, the model trained on ads data from one city has a successful prediction on the other ones. For example, the models trained based on the data of Tehran and tested on the data of Mashhad achieved 61% and 74% respectively in identifying street and neighbourhood. However, for some reasons such as imbalancement of datasets, data labeling challenges, and in some cases, identifying non-spatial n-grams due to clustering, precision has been decreased. Also, because of differences in urban patterns and place naming patterns between the cities, the recall has been slightly decreased.

A place can be referenced in two different ways: 1- By calling its name and 2- By coordinate data. Gazetteers are considered a bridge between that two types of georeferencing. According to the importance of these resources in geospatial applications, the enrichment of them is a necessity. For containing local place names, online property listings can be considered as a valuable resource for harvesting toponyms and enriching gazetteers. Regarding to that most users in publishing online property, ads consider a neighborhood or main street name which is well-known for the readers, these place names usually are written without any clue for identifying a location in a text processing manner. The behavior with respect to a set of spatial statistics can be considered as a spatial signature to recognize an n-gram as a neighborhood or street place name.

One of the central issues in choosing housing (buying or renting) is having a high degree of access to various types of urban transportation networks. Because the low-income groups of the society use public transportation more for traveling in and around the city, and this issue is so important in choosing a place of residence that it is one of the main local quality indicators and an effective factor to save the household budget, especially for the low-income sections of society. Therefore, the development of a score-based system and scoring of real-estate and housing based on access to the public transportation system is valuable. Analysis of previous research shows that the importance and impact of transportation systems in property valuation, from the distant past to the present and, as in the past, has a decisive effect on property placement, especially in the selection of residential areas; In this regard (based on the summary of the researches - Table 1) can be categorized and proposed in several scenarios. Scenario 1. Survey and ranking using socio-historical intelligence:In the past, human memory and quotes from others about the tranquility of neighborhoods were questioned, and this method is still common among many traditional communities. This type of ranking is qualitative and the indexing is very low or the parameters have not been quantified yet. So that in no visible place has it been given a privilege except in the public mind. In this method, it is evaluated only based on the needs of the society and the demand of the people and the type of their behavior with the environment. Here, the desirability of the place is considered according to the social desirability and without the desirability of other parameters. And it is a kind of use of public trust in society. Scenario 2. Review and rank the parameters based on the value from the customer's point of view:Determine the value from the point of view of a particular person or group without considering the acceptance of the community. Customers who value the proximity to their place of business value the property more without the need for transportation services. Scenario 3: Review and ranking with the characteristics of the type of building and local coordinates (local):In such a case, the price evaluation is based on the characteristics of the neighborhood such as connection and density of streets, parking, garbage disposal, etc. Also, determining the price of a house is related to the type of building (apartment or villa), size and age of the building, number of rooms, water system, gas, swimming pool, heating services, cooling, etc. Scenario 4: Review and ranking with purely economic parameters:Based on the economic issues raised in the study community. This means that the value of the property is considered based on proximity to commercial real-estate or suitable job opportunities. On the other hand, the income and financial capacity of each person determine these types of values. Scenario 5: Review and ranking with comfort-oriented parameters:Here the value of the property is a function of other determining parameters, since the relaxation and comfort coefficient of each person is different, the criteria of comfort and relaxation, change over time and the value of the property will also be subject to these changes. For example, for one person with a student, proximity to school and educational services is a priority, and for another, distance from school and the resulting congestion is a parameter of comfort. Scenario 6: Survey and ranking with the parameters of social welfare and quality of life (social life average):One of the most important criteria for valuing property are human welfare factors, including proximity to the urban transportation system (Metro, Buses and BRT), education, health care, employment and leisure. Meanwhile, in densely populated cities, the urban transit system is an important factor in improving the quality of human life and easier access to amenities. Even the distance from the property to the transportation system will significantly determine the value of the property. In this study, while reviewing examples of previous studies, their methods and results were specifically categorized in the above six scenarios, then by determining and classifying the indicators of each scenario, a new scenario called scoring and ranking with temporal and spatial variables (spatial effect of parameters and combination of previous scenarios) were presented. Also, the useful access radius of transportation stations was identified and verified based on the study of previous researches, consultation with specialized (academic) experts and local experts; then, using the fuzzy method, a mechanism for determining the characteristics, ranking and scoring was designed to create a hierarchical-scoring system with the algorithm of combining multidimensional real estate scores. This ranking provides a quick and easy comparison of residential properties in multiple locations in the city in a reliable environment. The proposed system has three layers: data, logic and representation; In the coordinate data layer and descriptive and analytical information, the properties in the study area and metro, bus and BRT transport stations are recorded. In the logic layer, the fuzzy web service is used for fuzzy inference and the web server is used for the display layer to interact with the data layer. The results of the evaluation of the designed fuzzy scoring system, in comparison with the field evaluations and the results of other systems, confirm the efficiency of this system. Scenario 7: Review and ranking with temporal and spatial variables (spatial effect of parameters and combination of previous scenarios):The effect of parameters valuing property, based on the need that exists in a particular place or time. For example, for the needs of a customer, which is valuable only in a limited time and place, the use of public transport system, as a result of the spatial impact of such factors, depending on the time and place of customer needs, definition and specialized scoring (Non-public) is presented. Sometimes a particular job situation requires a client to prefer a particular location only in certain seasons. Also, maybe the buyer or lessor of the property, each of them considers different and various parameters as value. As a result, for a person who is going to live in a place for a long time or a person who uses that place temporarily and for a short time, a series of short-term cross-sectional and long-term stable parameters will affect the value of the property. As a result, different results at different times and places provide maps for decision making. Given the above, it is necessary to emphasize that the scenarios have integration and correlation and all of them have weaknesses, the structure and array of measurement, quantification and indexing systems, due to the lack of the same regional, urban and meta-indicators, their performance on different scales is somewhat problematic. On the other hand, the indexing system is largely conventional and varies from place to place and in each geographical area. Therefore, the system of these models cannot be the same everywhere unless we can change the indicators. Table 1 summarizes the articles of various researchers in this field with emphasis on access to the public transportation system. Understanding the significant impact of the transportation system on property prices and its undeniable effects on the well-being and quality of life of people, designing a web-based system with dynamic and changeable information is important for public access. In our country, there is no comprehensive system that contains property price information with emphasis on access to the transportation system, so the development of a web-based system for rating residential real-estate based on transportation, as the goal of this study, is important. Especially to design a system that maintains its dynamism and capabilities due to the dynamism of urban development plans and plans, diversity and multiplicity of laws, the existence and intervention of various decision makers in urban affairs and the sharp fluctuation of housing and property prices that do not make gross errors during the oscillation periods; Also, the designed system can have its unique features, the possibility of registering the property by users and the possibility of displaying it on the map and viewing transportation scores.

Forest roads are essential for forest management, forest harvesting, wood transportation, recreation, education, research, and forest protection. To meet these needs, forest road networks have been constructed in the northern forests of Iran. Forest road mapping especially over large and mountainous areas is time-consuming and expensive. Today, remote sensing data can be considered as an important tool for forest roads extraction. Therefore, in this research, LiDAR data and UltraCam images were applied in order to extract hidden forest roads. At the first step, noise points in the point cloud data were removed. Then, according to the Central Limit Theorem (CLT), the third statistical moment (amount of skewness) of the data was calculated and the non-ground points were eliminated. At this next step, a number of non-ground points were identified as ground points. In order to eliminate these errors, slope-based algorithm with a radius of 10 meters and a slope of 22 degrees was applied on the points obtained from the first step, these points were eventually removed and the ground points were extracted. Then, extracted ground points were converted to grid. Then the grid was converted to polygon based on the pixel density, by using the DTM as well as UltraCam aerial images, polyglots that were not related to the road were removed. Until this stage, the output was the roads that were not hidden by the forest canopy. Therefore, the hidden parts of the roads were extracted by applying slope-based algorithm with the radius of 10 meters and 65 degrees slope on the whole LiDAR points and interpolating the results by spline interpolation method. By connecting and modifying the polygons, 3m wide dirt roads and 2m wide skidding roads were extracted. The results are evaluated by comparing to manually acquired road data. The quality measures completeness, correctness and quality were 82%, 86% and 72%, respectively.

So far, a lot of research has been done to assess the resilience of the city against a variety of natural threats such as earthquakes, but research on the assessment of resilience of urban areas against man-made threats, especially war threats, is far less that in this study Is. In fact, what poses a serious threat to military strikes is the unpreparedness to deal with them, and the best way to deal with these threats is to build and maintain preparedness against them. One of the main ways to prepare for crises is to be aware of the city's resilience in the event of a crisis, in which case, by adopting strategies, preparedness for such crises can be greatly increased. In the present study, the physical resilience of the seven areas of Babol has been evaluated from the perspective of passive defense using geospatial analysis and PROMETHEE II technique. In this study, first, using the opinion of experts in the fields of urban planning and passive defense, the basic threat of the city of Babol (air attack) was selected. Then, based on this and by studying previous research and obtaining the opinions of experts in the field of urban planning, passive defense and structure through interviews and questionnaires, sixteen criteria affecting the physical resilience of Babol in three categories of distance from special uses, access to main services and features the urban physical tissue was extracted and weighed. These criteria are: Distance from military bases, Distance from key stairs, Distance from refueling centers, Distance from the utility network, Distance from industrial centers, Network access the main way, Distance from fire stations, Access to medical centers, Outdoor access, Degree of confinement, Build density, population density, Number of floors of buildings, Skeleton type of buildings, Granulation of parts, Age of buildings. Then, the raster criterion maps were generated and the average values ​​of each criterion for each of the seven districts of the city of Babel (as alternatives for multi-criteria decision making) were calculated and the decision matrix was created. By generation the decision matrix and using the PROMETHEE II method, the 7 districts of Babol city were ranked based on the degree of resilience and a physical resilience assessment map of Babol city was generated. The results showed that different areas of the city of Babol do not have the same resilience, so that the central areas of the city and to some extent the southern areas of the city have lower resilience than the northern, eastern and western areas. In general, with increasing distance from the city center, resilience increases, which is less felt in the southern direction. Although the city of Babol has moderate to high resilience in general, but by resilience analysis, the most important reasons for the low resilience of the central areas of the city, namely regions 4 and 5, can be obtained. Analyzing the research results by passive defense experts, it was found that the most important reasons for low resilience in the central urban areas of Babol (areas 4 and 5) are high construction density, high degree of confinement, important military centers and numerous refueling centers in these areas.

Nowadays, surface roughness measurement has been considered in many civil and industrial applications. One of the important indicators in measuring the roughness of the surface, is determining the fractal dimension. To determine the fractal dimension, the profile meters are usually used by contact method, which in the case of surfaces with low roughness may cause distortion of the surface texture and as a result, the roughness is measured with low accuracy. In this study, a fast and reliable method based on close range photogrammetry, which is a non-contact method, for measuring fractal dimension and roughness is presented. The case study in this research is the sand surface.. Digital elevation model was created by taking several overlapped photographs. For accurate measurements on the surface 6 control points were created, then scaling and definition of the coordinate system was performed. The accuracy in x-y plane is 1.33 mm and the height accuracy is 0.32 mm. In order to measure the surface roughness parameters including correlation length and root mean square height (rms-height), several profiles were extracted from the three-dimensional surface. The slope of the surface spectrum was calculated using Welch method for each of these profiles then the fractal dimensions were calculated. Finally, using the fractal dimension, the correlation length and rms-height were calculated for each of the profiles. In this study, surface roughness was evaluated using four indices of fractal dimension, correlation length, rms-height and ZS parameter (ratio of rms-height to correlation length). ‌ The results showed that for all profiles, ZS with 81% correlation coefficient with fractal dimension, is a sufficient indicator for calculating the surface roughness, because of the the independence of this indicator from the length of the profile. This study showed that the close-range photogrammetry is a very suitable and reliable method for measuring roughness parameters. One of the important advantages of this method, unlike other methods such as needle or laser profile meters, is having multiple profiles in any desired direction and preserve of texture (especially on very low roughness surfaces) due to direct contact of the profile meter with the surface.

Energy is an unavoidable requirement for humans. However, due to population expansion, the energy source is expected to become limited in the next years. As a result, consumers favor renewable, clean, and cost-effective energy sources. Unfortunately, there is no one source of energy that can fulfill these needs. Within the framework of a long-term and strategic process, determining the right energy policy problem with interactive criteria and alternatives can be seen as a multiple criteria decision making (MCDM) problem. One of the features of Iran's energy system is its reliance on imports. Different sets of decision-makers are engaged in the process of picking one of several Renewable Energy investment projects. Because of the increasingly complicated social, economic, technical, and environmental elements at play, decision-making must take multiple competing agendas into account. Traditional single-criterion decision-making is no longer capable of dealing with these issues. The VIKOR technique, also known as the Compromise Ranking method, introduces the Multi-criteria ranking index based on a specific measure of "closeness" to the "ideal" answer. The approach is used with the Analytical Hierarchy Process method to weight the relevance of the various criteria, allowing decision-makers to give these values depending on their preferences. The integration of renewable energy supply systems into energy supply networks is a critical lever for addressing the issues of sustainable development and climate protection. The synthesis issue, on the other hand, is an intrinsically complex process for which three hierarchically dependent layers must be considered. The configuration level is where equipment selection is made, the sizing level is where equipment capacity is calculated, and the operational level is where real load dispatch is specified. While these levels must be considered for any energy system, dealing with the complexity resulting from the temporal and spatial interdependencies associated with renewable resources, which usually necessitates the installation of storage systems, is a key challenge in the synthesis of renewable energy systems. Furthermore, the diversity of available technologies and conceivable combinations adds to the complexity. Furthermore, because the adoption of renewables is still often motivated by environmental concerns, both the economic and ecological consequences must be addressed. Thus, complicated linkages and trade-offs between technological, economic, and ecological implications must be weighed in order to identify the optimum solution for a specific synthesis challenge. This study presents a synthesis approach for determining optimal solar and geothermal sites of the output layers of the Surface Energy Balance Algorithm for Land (SEBAL) algorithm, as well as a multi-criteria analysis of various environmental, socioeconomic, remote sensing data, and spatial information system. Identifying both prospective solar and geothermal locations together or adjacent to each other can not only provide a complementary output, but by combining these two energies, it addresses the deficiencies in each of their separate performance. The designation of such regions necessitates a thorough understanding of their efficacy elements and criteria. To that aim, fifteen distinct data layers, as well as Landsat 8 satellite imaging, were used in northern Iran over two periods of cold season for ground energy and warm season for solar energy, digital elevation model and its derivatives. First, multiple types of study data are quantified, weighted, and then merged. Following that, for economic evaluation of the results, the two main factors, demographic-industrial centers and development of these centers, are experimenters and analyzed, and become a more suitable construction of power plants were classified into five classes: poor, medium, appropriate, and very appropriate. Separate analyses for solar and geothermal energy show that about 51% and 30% of solar and geothermal energy, respectively, are found in acceptable and extremely suitable regions. Furthermore, when economic considerations and population-industrial hubs are taken into account, the combined findings of these two energies show that almost 59% of the territories represent areas prone to solar energy and geothermal. These locations in the province's south, southeast, and central regions, as well as the province's north and northwest, have high potential for geothermal and solar energy.

Due to the rising global temperature, especially in cities, which is often due to urban and population growth, followed by life, financial and environmental risks, identifying the factors affecting the Land Surface Temperature (LST) in urban areas is of great importance. Rising temperatures in urban areas have created a phenomenon called Urban Heat Islands (UHI), which is a very dangerous phenomenon for humans and the environment. Therefore, by identifying these factors, we can prevent this phenomenon as much as possible by using public education to the people, enacting effective management laws and policies, and more monitoring to deal with the stimuli of rising the LST. The main reason for the increase in LST in urban areas is due to urban growth in two-dimensional and three-dimensional direction, and this phenomenon is intensified by population growth and land use changes. Numerous other factors, such as the heating of buildings, air pollution, and the use of unsuitable materials such as asphalt, which absorbs sunlight, cause rising the LST in the streets and alleys. The purpose of this study is to identify the spatial factors affecting the LST in urban areas during a certain time and predict it based on the effective factors determined by the proposed algorithm. In this study, a selection method for identifying the effective factors in predicting Land Surface Temperature in urban areas through the combination of Genetic Algorithm and Geographically and Temporally Weighted Regression was presented. To evaluate the proposed method, nine factors including land use, distance to roads, population density, construction density, air pollution, aspect, slope, building height and elevation were used in this study as the spatiotemporal factors to predict Land Surface Temperature in a desired time in Tehran city, Iran.

In this study, an attempt was made to identify and investigate some factors affecting LST in urban areas. Attempts have also been made to consider both natural and human factors. These factors include elevation, aspect, slope, land use, distance to roads, population density, construction density, air pollution and building height. In order to find the most effective factors on LST, a combination of genetic selection method and Geographically and Temporally Weighted Regression (GTWR) was used in which LST is estimated through the spatial data during a certain time. . The optimal factors are selected to predict LST by combining the genetic algorithm with the GTWR function. By the development of the time dimension of the GWR model, the GTWR model was introduced, which includes bandwidth and the spatio-temporal kernel function. In Geographically and Temporally Weighted Regression, it is assumed that data have autocorrelation and temporal instability in addition to spatial autocorrelation and instability. That is, regression coefficients, in addition to changing from point to point, also vary for one point at various times.

In order to implement the proposed algorithm, 1500 points were randomly generated in the study area. All input data in ArcGIS software was converted to raster format so that the values of this data could be assigned to these random points. After preparing all data based on the generated points, these data were entered into the proposed algorithm and finally 5 factors out of the 9 factors were identified as effective factors. The value of R2 obtained by GTWR method with 5 identified effective factors was 0.9882 and using all available factors 0.9621 which shows the improvement of the results and high compatibility of this model with selected factors. Also, the RMSE value was 0.5097 and the normalized RMSE value was 0.1908, which indicates the high accuracy of this model.

Using the proposed algorithm, 5 factors including elevation, distance to roads, land use, construction density and air pollution were selected as effective factors for predicting LST. The results of this study indicate that the prediction results using the selected factors are more accurate than the same results using all factors. Accordingly, the experimental results of this study show the appropriate performance of the proposed method in predicting LST. Finally, due to data availability problem in this study, it is suggested to increase the number of factors in study area, especially human factors in future works. Traffic in cities, urban geometry, types of land uses are among the important human factors that can be effective in LST.

Knowledge of underwater features and ocean bathymetry plays a significant role in coastal engineering and management, resource exploitation, navigation, research on tide and biodiversity, planning for seawalls and wharves, offshore fisheries, and aquaculture. Shallow water depth surveying is essential for the protection of vessels during navigation. Conventionally, depth charts are obtained by sonar measurements carried out from dedicated vessels, which are expensive and time-consuming, and problematic in shallow water areas. Remote sensing techniques may offer an alternative method that can be used to reduce the cost and labor needed for underwater topographic surveying and mapping. Therefore, remote sensing, due to its high ability to collect information in a short time and a wide geographical area, will be a very suitable solution for many studies and engineering projects in coastal areas. This issue is critical in Iran, which has a long coastline. In this study, an attempt was made to prepare a depth survey map of the coastal areas of the Oman Sea coast of Jask port region by implementing the swell wave spectral pattern analysis algorithm on Sentinel-1 satellite images. Synthetic Aperture Radar (SAR) is an active remote sensor that is fairly weather insensitive and has global coverage. It provides two-dimensional (2D) information on the ocean surface. Under favorable conditions, it can detect topographic seafloor features in shallow water areas. This paper presents a practical method for shallow water depth estimation based on swell patterns in SAR images with two different data for wave period. This method is based on the refraction and wavelength changes of swell waves in coastal waters and can measure depth in medium-depth waters. The Linear Wave Theory in waters with medium depth establishes a relationship between depth, wavelength, and period of swell waves. Two-dimensional Fourier analysis was used to calculate the wavelength of swell waves, and the accuracy of calculating the wave peak was increased by applying a frequency filter. One scene of Sentinel-1 SAR image over the Jask coastal region is chosen for investigation. The estimated water depths from the SAR image using the ERA-5 wave period are compared with the Bathymetric map of Jask. The average absolute error is within 0.992 m, with root mean square error within 1.361m with the average relative error less than 6.05%. The estimated water depths from the SAR image using the estimated wave period from the deepwater image are compared with the Bathymetric map of Jask. The average absolute error is within 0.811 m, with root mean square error within 1.125 m with the average relative error less than 5.051%. This indicates that the estimated wave period from the deepwater image is better for water depth estimation than using the ERA-5 wave period. Also, Sentinel-1 SAR can detect wave shoaling and refraction in depths between 10 m and 25 m because of its high resolution. The method can be used for water depth with a 10 m detection with reasonable accuracy within the test area. Using Spectral Analysis in Deep Water Range make 17% improvement in depth derivation Accuracy Compared to the Global ERA-5 Wave Model in the Range of 0 to 25 Meters, and 30% Improvement in the Range of 20 to 25 Meters.

Population growth and increased migration from rural to urban areas have led to widespread climate change that has significantly impacted land use. Urban sprawl is a phenomenon that happened these years, especially in developing countries. Therefore, planners have always been looking for methods and models that simulate the expansion of urban and climatic land use well to prevent the unbalanced growth of cities, climates, and undesirable development problems. These models guide them to manage the plans in the desired direction. Advances in artificial intelligence in recent years, along with widespread access to online data, the emergence of new methods of big data analysis, and the development of advanced technologies, have led to the emergence of new technologies and methods such as machine learning techniques and agent-based modeling. Investigating Iran's policies on land use issues and developing new solutions with considering a comprehensive review of data-driven methods are needed to analyze the problems and solve the problems resulting from these changes. In addition to data-driven approaches, the specific benefits of factor-based models include their ability to model individual decision-making institutions and their interactions, the combination of social processes and non-monetary influences on decision-making, and the dynamic linkage of social and environmental processes. Therefore, classification, forecasting, modeling, and simulation to estimate the future situation with the help of data from these changes in different periods can be the basis for making the right decisions in the current situation. In this regard, experts in this field have always considered the use of new strategies for land modeling and land use planning. Although extensive studies have been conducted in the field of machine learning (ML) methods as a new approach to classification, prediction, simulation, and modeling in various fields of science; However, these studies have less reviewed the proposed and applied methods of the agent-base modeling and machine learning in the analysis and modeling of land-use change studies. To this end, this article provides the opportunity for a systematic review of the application of machine learning algorithms and agent-based modeling, which has been recorded in the most critical research and experimental evidence of the United States, Europe, and various parts of Asia, especially East Asia and also Iran. Therefore, the different algorithms and methods implemented in each study are reviewed, and the results of data analysis are presented accordingly, which can be the basis for further research to use widely used, accurate and dynamic models. This study shows that different land use issues such as classification, forecasting, and simulation require algorithms with appropriate structure. Results show that no method and algorithm can be considered absolutely superior compared to other methods and algorithms. Thus, the most widely used methods for classifying, predicting, and simulating land-use change are categorized in this paper. In general, it was also found that support vector machine (SVM) and Convolution Neural Networks (CNN) as widely used methods, with the best results, provide valuable solutions for land use classification, forecasting, and simulation.

One of the significant dangers to human life is crises and natural disasters worldwide every year. If such incidents are unpredictable, their risks and casualties will be much higher. Among disasters are floods, hurricanes, volcanoes, earthquakes, tsunamis. An earthquake is an event that is more prevalent than other disasters and is almost unpredictable. Respond structure to crises and disasters is called crisis management which deals with all issues before, during, and after crises and disasters and leads to activities in the field of planning, preparedness, prevention, response, and reconstruction. One of the most basic and essential things that can reduce the casualties of various events is disaster relief and rescue, which respond to the crisis management structure. Also, the contextual information of the environment, rescuers, and activities created a context-aware recommending system that can facilitate the process of interaction with the environment. This study has checked the types of contexts, their relationship, and the structure of earthquake rescue in Iran, where there is a significant crisis due to geographical location and seismicity. The whole problem space consists of three parts to provide a meaningful definition of the concept of "context" in the deployment of relief and rescue teams. The rescuer is the main context. Relief and rescue team as the object's environment, which includes team information of rescuers, consists of team members' position, distance, physical condition, and activity compared to other rescuers in the group. The physical environment is a collection of injured people, buildings, and relief and rescue teams in a specific area. Contexts of rescuers and their relationships, teams, and the hypothetical earthquake were studied by studying related articles and books and interviewing experts in the field of research. The study of context-aware and optimization methods used for the actual structure of rescue teams are the innovations of this study. Contexts during relief and rescue include location, time, the extent of human and building injuries, rescuers' interactions with and with the environment, and activities, rescuers' specialties, and priorities. Interaction is necessary for optimal management of relief and rescue. To create a relationship between the various contexts and optimize the relief and rescue process by defining the mathematical function and using sensed information from contexts into the proposed optimized algorithm. Finally, the solution has been designed and implemented with an ant colony algorithm and geospatial information system to optimize the allocation of rescuers to the affected areas and the necessary activities in the part of Tehran. The use of combination context-aware and artificial intelligence algorithms for the subject of relief and rescue in earthquake crisis is new research that Led to a 1.79-fold improvement of the proposed solution compared to not considering the existing contexts in relief and rescue without using artificial intelligence algorithms. So can be created a context-aware system based on the appropriate optimization algorithm as a suitable solution to the problem of post-earthquake relief and rescue. Due to the context structure in this research in the individual's activity effectiveness on other individuals and groups, the ant colony algorithm is a collective intelligence base that can provide optimal positioning in a discrete environment. It allows more repetition in less time than other algorithms.

Hyperspectral and LiDAR data provide spectral and height information and they have high potential in classification of complex urban area. This paper proposed meta-heuristic method in feature level fusion of them. For this purpose, a comprehensive spectral-spatial-structural feature space is generated based on feature extraction method such as spectral indices, texture analysis, roughness, etc. Previous methods apply just one criterion to evaluate classification performance. However, in the proposed method, three criteria including generalization ability, classification complexity and classes separation are considered. Multi-Objective Particle Swarm Optimization (MOPSO) is implemented to select optimum feature space and Support Vector Machines (SVMs) parameters simultaneously while optimize all three parameters. The obtained results show the proposed method increases classification accuracy up to 11% and 58% respect to hyperspectral imagery and LiDAR data by eliminating 300 features (among 611 feature) and also increasing classes separation.

In today 's world, optimizing a successful business depends on using all the resources that make it superior to its competitors. Location-based marketing or Geomarketing leads to critical and effective decisions by analyzing different geographical areas. Spatial information systems marketing is more agile in strategic decision making. In this age where data dynamics are so important, the use of spatial data infrastructure (SDI) can create a platform for spatial data sharing. Spatial Data Infrastructure (SDI) with instant sharing of spatial data can provide a dynamic platform. SDI-based Geomarketing fixes the flaws and shortcomings of spatial information layers in GIS-based Geomarketing. The main advantage of this model compared to previous models, in addition to information dynamics, is that there is no need for an operator to record and store information and produce layers of location-based information in alternating time periods. This is an applied research in terms of purpose and is based on a descriptive method that includes a set of methods that aim to describe the conditions or phenomena under study. In terms of implementation, part of this research is collected in the form of libraries and documents using the theoretical foundations and background of previous research, and the other part is done experimentally and by collecting information from the base statistical reference authorities.Accordingly, in this research, a new model of location-based marketing is presented, which uses spatial data infrastructure for the first time. In this article, we seek to answer the questions of whether the use of Geomarketing based on spatial data infrastructure has an advantage over GIS-based location-based marketing? Is it possible to prioritize the optimal areas by sharing important indicators from different databases of executive agencies in the field of marketing of Semnan banks? In this regard, using this model and based on the data available in 4 databases of related executive agencies, the city of Semnan is divided into 139 urban areas or statistical areas. Afterwards, using the geoportal infrastructure of Semnan province spatial data located in the Management and Planning Organization of Semnan province, the desired registration information layers were shared and model’s maps were extracted. Subsequently, by examining 150 demographic and economic indicators and examining their correlation coefficient with the number of bank branches, it was found that the indicators of literacy rate, household size, population density, distance from the city center, number of important businesses, income decile, number of apartments and number of schools are most relevant with the number of bank branches in each region. Then a model was estimated using multivariate regression. In this model after estimating the model coefficients, the number of businesses index with a coefficient of 0.598 has the greatest impact on the number of bank branches. According to the results of this model, area No. 62 of Semnan city has the most favorable conditions in terms of banking marketing indicators. So the main advantage of this model compared to previous models, is that there is no need for an operator to record and store information and produce layers of location-based information in alternating time periods in addition to information dynamics. In this model, a dynamic model can be achieved by using dynamic information by sharing layers of spatial information in the context of spatial data infrastructure, in addition to maintaining the intellectual property of information. This research is supported by the GIS unit of the Management and Planning Organization of Semnan Province in Iran.

Today, radar systems are used in a variety of applications including remote sensing. One of the most important challenges is to determine the position and the displacement vector of targets with high accuracy. For this purpose, the sensors installed on the ground platforms are used. In this paper, a method is presented that determines the absolute and relative position of the targets and also estimates the displacement of the targets in three dimensions. An FMCW mm-wave ground-based MIMO radar that has two transmitter antennas and four-receiver antennas are simulated. The sensor is designed to move on a two-dimensional plane and received the echo signals which are propagated by transmitters. Using the phase information of the signals captured by receivers antennas in different positions of the sensor, the arrival angle of the signal reflected from the target to the receiver antenna is determined. Then using the least square adjustment equations, the absolute location of the target is determined in the 3D space. Two scenarios were performed to evaluate the proposed model. The first test showed that the RMSE of the absolute position determination is less than 3 mm. in the second scenario, the determination of the displacement vector of the targets is examined. The result shows that the RMSE of the estimated displacement vector in three dimensions is less than 0.1 mm. It was also shown that the proposed method has no limitation in determining the relative displacement with different sizes. The presented model has the ability of the large displacement estimation without using phase unwrapping algorithms.

One of the advantages of the remote sensing method is that it minimizes surface surveys, especially in inaccessible areas based on spectral information obtained from satellite images. The presence of minerals can be explored by their spectral signatures recorded in satellite images. The main hypothesis of this research is that the combination of such processes with agent-based modeling (ABM) can lead to better planning of the mining exploration and reduce cost and time. In this study, ASTER and OLI sensors were used to identify areas containing minerals in the Yanesar section of Behshahr city. Due to the combination of mineralogy and lithology, the study area is mainly made of lime, shale, clay, and marl. The result of the processing is the exposure of lime units. After pre-processing on the information and satellite images, band ratio methods have been used to identify areas with lime mineral potential. Then, Spectral Angle Mapper (SAM) method was used to more accurately separate these areas using the USGS laboratory spectral library. In order to optimize time and cost in order to identify areas containing lime minerals, agent-based modeling has been used as a new approach. By considering several strategies based on random movement and movement in mineral potential areas, time tables and cost were obtained and compared with each other, and finally, the best results in terms of time, cost, and a number of explorer agents were obtained.

The main goal of global navigation satellite systems is to determine the precise coordinate of points in all weather conditions. On the other hand, the waves transmitted from the satellites of these systems pass through different layers of the atmosphere such as the troposphere, and this leads to refraction in the path of the wave and ultimately delays in receiving the waves. Therefore, the study of the troposphere and its effects on the signals of the global positioning system is particularly important. The tropospheric delay is divided into two parts, dry and wet, which due to the dependence of the wet part on water vapor changes is the most challenging part of the tropospheric delay.  The aim of this paper is modeling of tropospheric zenith wet delay in Los Angeles in the United States, utilizing a combination of OLCI sensor data of Sentinel-3 satellite with high spatial resolution and data from 97 high temporal resolution GPS stations in this area. The least-squares collocation method is applied for spatio-temporal modeling of the tropospheric zenith wet delay. In order to estimate the trend in the least-squares collocation method, a combination of data from the above two sources was used and a four-dimensional (temporal-spatial) surface was fitted to the tropospheric zenith wet delay data obtained from the OLCI sensor and GPS stations. In the estimation of the observation signal part, instead of considering a spatial-temporal covariance function, the modeling time interval is divided into smaller sub-intervals and the spatial covariance functions are estimated in each sub-interval. The results of the article method and Sastamoinen model which uses the analyzed GFS data were compared with control points values. The RMS of estimated zenith wet delay in control points by least squares collocation was 1.86 centimeter, while the RMS of the Sastamoinen method is 15.97 centimeters.