نشریه پژوهش های سنجش از دور و اطلاعات مکانی
شماره 0 (پیاپی 1، زمستان 1401 و بهار 1402)

The increasing population of the large cities has led to developing new constructions in areas around cities to create settlements for the overflow of the population.Such changes in the natural land cover not only disturb the heat balance, but also have negative effects on the landscape, energy efficiency, health and quality of human life. Therefore, it is important for urban planners and managers to be aware of the changes in land cover and land use, especially in metropolitan areas, during long-term periods of time, in order to evaluate and predict the problems caused by these changes. Multi-temporal remote sensing data are one the powerful tools fordetecting land use/cover changes due to the increasing urban growth and then, for updating the three dimensional city models.

In this paper, the impact of Mehr Pardis housing construction is investigated on the land use/cover changes. The proposed land use/ cover change detection strategy in this paper is a post-classification method based on performing object based image analysis procedure. For this reason, Landsat satellite images have been used in 17 years’ time interval, between 2002 and 2019. After performing initial image processing and image segmentation, the three object classes of residential buildings, vegetation, and soil were identified by the object based image analysis procedure. Then, post-classification change detection performed on the generated object based classification maps of both 2002 and 2019 epochs. For change detection in this research, while comparing and contrasting the classes of recognized objects in the classification maps, the results of revealing the changes in the environment, including determining the amount of increase in constructions, changes in the area of soil and vegetation It is obtained.

The produced change map and statistical analysis of the post-classification change detection results reveals that the soil object class is decreased for about 17% and built up areas are increased for about 184% in the 17 years’ time interval. Agricultural fields in this study area are mostly destructed due to the developments in constructing built up areas. The increasing amount of about 104% in vegetation covers relates to the trees and grasslands in new constructed built up areas. To evaluate the obtained results of changes detection in this research, the evaluation of classification maps was used. In this regard, the values of the overall accuracy and Kappa coefficient of the land cover/use classification map in 2002 were 98.41% and 0.86, respectively, and for 2019, 97.01% and 0.87, respectively. Using the capabilities of the object-based analysis method in this research, along with the 15-meter spatial accuracy of the Landsat images, made the classification maps have an acceptable accuracy.

Due to the fact that construction is associated with changing the ecosystem, the construction of housing units in Mehr Pardis has led to the destruction of the mountain environment in some areas and the loss of vegetation in other areas. It is illustrated in the produced land use/ cover change map between 2002 and 2019 that the constructions are rapidly increased in Pardis area and this causes the serious impacts on the environment.

Today, urban management is one of the most important issues for decision makers and managers in urban areas. The problem of finding the shortest route, as one of the important issues in the field of urban management in order to reduce the travel time between two critical points, has always been the focus of many research fields such as urban management, transportation, communication, etc. During the past decades, the genetic algorithm has worked well in solving complex multi-objective optimization problems, but many genetic algorithms are only suitable for finding the optimal route in local networks. In these networks, if the number of points increases, algorithms will not be effective. The purpose of this research is to find a route in the street network of Tehran that has the least time, distance and cost. Therefore, a new method is proposed to solve the routing problem based on genetic algorithm, with the assumption that all lines in the network have positive weights.

The charactristics of the proposed algorithm is the variability of genetic algorithm operators, which is defined according to the network structure. Accordingly, the mutation operator in the genetic algorithm is defined based on the studied space and the distance between the start and end points. Integer coding is used to solve the problem, and therefore the points in this graph are named using integers and each person in the population is considered as an answer to solve the problem. The population size depends on the number of nodes in the graph and the length of each chromosome. The length of the selected strings is considered equal to the maximum number of nodes in the network, because there is a possibility that the best route is the route that passes through all the nodes. At the end, the proposed algorithm is implemented on the study area, which is a planar graph. The accuracy of the proposed method compared to the conventional genetic algorithm has been evaluated in three pairs of points.

Considering that the goal of solving the problem was to find the route that has the least weight, one combination operator and three mutation operators were presented in the proposed algorithm. This is despite the fact that in traditional genetic algorithms, only one mutation and combination operator is used. In this algorithm, the way to use mutation operators depends on the structure of the network and the distance between the start and end points. The use of the proposed genetic algorithm compared to the traditional genetic algorithm has been associated with a 16% improvement in performance, which shows that the proposed genetic algorithm reaches the solution of the problem faster. As shown in Figure 4, the best route is the route whose fitness function value is closer to one. The results of comparing the proposed method with conventional methods show 16% higher speed.

Considering that the initial assumption of this research was the positive weight of all the lines in the network, the mutation operator in the genetic algorithm was defined based on the studied space and the distance between the start and end points. The results showed that if there is a small search space, fewer points are needed and in order to generate the initial population, the nodes that are next to each other and closer to each other should be selected. In this way, the value of the fitness function of the people in the initial population increases and the answers become closer to reality. For future research, it is suggested that in order to generate the initial population, the points between the start and end points should be selected, and also the selected points should be near the connecting line between the start and end points, because when the weight of the edges of the network is the distance between the points, the best route is in the space between the starting point and the end point. It is also suggested to evaluate the performance of the network with several combination operators.

The urban deteriorated texture areas is without a doubt one of the most important challenges faced by urban areas and especially metropolises in recent decades and is considered one of the main concerns of urban management. By identifying the areas that need revitalization, reconstruction and improvement, it is possible to optimize the use of land in order to improve the quality of the neighborhood and create spatial justice, as well as prevent the linear growth of cities. Old textures of urban areas can affect social and economic activities. Therefore, urban deteriorated texture areas can affect citizens as one of the challenges of cities. The phenomenon of deteriorated texture in urbanization is considered as one of the challenges of advancing sustainable urban development. This happens for several reasons, the most important of which is the reliance on traditional and insufficient monitoring and classification tools. Despite the increasing number of these areas, sufficient efforts are not made for spatial monitoring and classification of these areas. The process of monitoring and classifying these areas is considered as the first step. This study is presented with the aim of determining the optimal use of the urban deteriorated texture areas in order to improve the physical appearance of the city, reduce traffic, improve the environmental conditions, increase the standard of living of the people, and provide all citizens with welfare, educational, and medical services, as well as adequate security.

In this study, by using cost optimization algorithms and AHP multi-criteria decision making model and integrating it into GIS system, it has been used to identify and prioritize the intervention in the blocks and urban deteriorated texture neighborhoods of Iran Gas in Qazvin city based on predetermined criteria. At first, we got a full understanding of the characteristics of this section to compare it with the whole city. In order to identify the criteria used in this research, we examined the main location factors for Irangaz neighborhood of Qazvin city. Then, the amount of urban deteriorated texture in Iran Gas neighborhood was calculated in two different dimensions of physical and functional urban deteriorated texture and the existing land use was evaluated. Finally, by identifying the incompatible blocks and the optimal blocks, the required maps for the management of urban deteriorated texture were produced.

In the study area, the urban deteriorated texture areas are monitored and classified into 4 classes: no deteriorated texture (zero to 50%), low deteriorated texture (50 to 65%), moderate deteriorated texture (65 to 80% wear) and high deteriorated texture (over 85%). The results showed that there are rarely blocks without urban deteriorated texture in this area, and by examining the causes of urban deteriorated texture, which were mainly the incompatibility of uses, the current situation should be re-evaluated.

In general, most of the tissues of the studied area have suffered urban deteriorated texture in recent years due to lack of planning and management, which is very important to prevent the aggravation of this problem and its impact on the neighboring tissues. Therefore, in order to achieve this goal, it is very important to collect information on the areas that are prone to urban deteriorated texture and to monitor the areas that are subject to urban deteriorated texture.

Remote sensing includes collecting information about the physical, chemical and biological characteristics of targets on the surface of the earth. In the last decade, Cube Satellites (CubeSats) have been transformed from pure educational tools to standard and inexpensive platforms for technology demonstration and placement of scientific-research payloads in Earth's orbit. Cubesat is a type of Nanosatellite consisting of cube-shaped units measuring 10×10×10 cm3 and weighing around 1.300 kg. The use of Nanotechnology for miniaturization of space-quality electronic components, cost and time reduction for the development of technologies related to the construction of satellite bus, and the possibility of placing a constellation of Cubesats in orbit with only one launch are among the advantages that companies and It has encouraged space organizations to design and build such satellites.

In this study, the technical characteristics, capabilities, applications and limitations of Cubesats for earth observation missions are reviewed, so that by evaluating these missions, a clear perspective can be imagined for the future of remote sensing applications for our country. In the beginning, a report on the development and new missions of spacecrafts of space organizations, universities and private companies is presented. After a comprehensive review on the capabilities of the Cubesats base and its potential limitations and effects on earth observation missions, the applications of the Cubesats are discussed. Finally, the results are compared with the technical requirements of earth observation missions and the capability of Cubesats satellites to perform these missions is evaluated. Thus, the possibility of carrying out several earth observation missions by Cubesats satellites has been identified, which are potentially compatible with the capabilities of this type of satellites.

Cubesats constellation transfer a large volume of satellite images to the ground stations, which produce remote sensing big data and therefore requires the use of artificial intelligence to calculate, process and store them. Low price, small dimensions, manufacturing and development cycle in a short time and reducing the revisit time are one of the most important advantages of Cubesats, which have enabled the monitoring of natural hazards and continuous monitoring of the environment. In addition to the simple architecture and the reduction of the development and launch costs of spacecraft, the need for less investment has made it possible for private companies and universities to participate in space missions. Unlike to the strengths of the Earth observation satellite constellations, we can mention the low radiometric resolution, the reduction of the signal-to-noise ratio and, as a result, the reduction of image quality in non-solar orbits, low spectral resolution and dependence on satellite and UVA images pointed out as their current weaknesses.

Real-time monitoring of natural disasters, forest fires, and flood mapping is one of the main applications of Cubesats images, which is possible due to high temporal resolution of Cubesats constellations. Also, desertification monitoring, drought monitoring, climate change monitoring and investigating its effects on the reduction of natural glaciers, tracking human activities and their environmental and economic effects, investigating light pollution in cities, expanding cities, estimating housing prices and monitoring marginalization in cities are other applications of Cubesats constellations. Currently, Cubesates images are used as a complement to the satellite images to improve the temporal resolution and to monitor specific areas in a specific time of the day and night.

Studying the emission of methane gas as the second most important greenhouse gas is vital due to climate change. This study was conducted to check the correctness of the news published by NASA regarding the existence of a methane cloud in the south of Tehran. Due to the importance of the subject, so far many remote sensing satellites such as sentinel-5, MethanSAT, SCIAMACHY, and GOSAT have been provided to monitor pollutants, especially methane gas. Also, much research has been done in evaluating this sensor and monitoring polluted areas. For example, Cherepanova, et al analyzed the spatial-temporal analysis of methane concentration and its relationship with burned areas. Using daily data from the TROPOMI sensor, they concluded that methane concentrations were higher in newly burned areas than in the previous year's burns. Maurya, N.K., et al investigated the concentration of SO2, CO, SOX, and CH4 pollutants using Sentinel-5P TROPOMI data in the periods after wheat and rice harvesting and land burning. The results showed that the concentration of CH4, SO2, SOX, CO, and aerosol increased significantly during the burning period of agricultural land after harvesting rice and wheat every year.

In this research, the daily data series of the TROPOMI sensor on the Santi-5 satellite were used on the Google Earth Engine platform. First, the spatio-temporal analysis of methane dynamics in Tehran has been discussed. In this way, time changes are made from July to October 2022 and also for the spatial analysis of pollutants, a monthly map has been made. Then according to the obtained date, the correctness or incorrectness of the presented map has been checked. Finally, in order to evaluate the results, the length of the methane cloud observed by NASA was compared with the methane cloud extracted by TROPOMI.

The obtained results showed that on September 1st, 2022 methane concentration was high and around 1970 ppm, and other gases did not have a high value on this date, which indicates the lack of effect of other gases on the increase of methane concentration and lack of misdiagnosis and according to the average map in the south of Tehran This gas has been more. Also, the methane cloud observed by NASA on the old Tehran-Qom road near the Qasemabad-Shorabad neighborhood was confirmed by the use of Sentinel-5 satellite data on the Google Earth Engine platform in red, meaning high concentration, which caused the release and concentration of methane gas. It is located in the south of Tehran and its main source was the landfill in this area. In addition, the length reported by NASA was compared with the length of the methane cloud extracted from the TROPOMI instrument, which was 4.8 km and 4.41 km, respectively.

Nowadays, air pollution has become a major problem due to environmental problems and adverse effects on human health, especially in densely populated cities. Based on the obtained results and the image of methane gas on September 1st in the south of Tehran, it was found that the news published by NASA was true and the main factor was that this area is a garbage dump. In this way, the Google Earth Engine platform can be used as a suitable tool for monitoring daily, monthly and annual changes.

Geospatial Information Systems has developed their applications in varioua area of science. The reason for this development is that Geospatial Information Systems are based on spatial data while nowadays spatila data is a major porion of information. One of the areas of wide use of Geospatila Information Systenms is Transportation and Traffic. It is obvious that Transportation and Traffic is dependent on Geospatial Information. In this paper the public multi-modal transportation in the city is studied. In cities, people may use private vehicles (such as automobile or motorbike) or public vehicles (such as bus, subway, etc). The city managers always aim to engage people to use public transportation system because extensive use of private vehicles can lead to various problems such as air pollution, noise, increasing fule consumption etc. Public transport only runs on a certain route and so many travels within the city needs use of more than one public vehicle. Thus, a traveler in a city may be obliged to use several and probably dissimailar public vehicles. He/she may also traverse a short path on his/her feet. In this situation we are facing a multi-modal transportation problem on a spation framework which can be solve by multiple methods considering various criteria. Such a problem has a good research background. However, in Iran, especially in Qavin city (the case of this study) such a system does not exist.

In this study the agen-based modeling is applied to simulate the travels of people in the city by public transportation system. The environment is the map of street network of Qazvin city which the routes of public transportation system have layed on it. The public transportation system in Qazvin city includes taxies and buses plus telephon taxi agencies. In this mode, the agents play the role of the travellers who travel from a specific point to another specific point in the city and have various options of using transportation system. The agent is able to use all of the options of travel and calculate the duration and the cost of each one to determine the mos desirable option.

The results revealed that public transportation system of Qazvin city have various desirability in its various routes. Thus, it is etter to estimate the desirailty of each route separately. However, generally speaking, we can argue that in the short travels, taxi is a good option, in mean travels the bus is affordable and for long travels using telephone taxies may be convinving. Nowadays, internet taxies are widely used. Internet taxies may be considered similar to telephone taxies. The difference between internet taxies and telephone taxis is that internet taxies are usually more abundant and also chepaer than telephone taxies.

Deployment of agent-based model as a simulator model creates a lot of power to analyze the problems of transportation system and traffic. This study proved this claim. The results of this study provide a general view of the cost and speed of public transportation system of Qazvin city while any traveller may have his/her specific idea. For instance, some people may choose the bus for their travel because of their own economical or security reasons. Nevertheless, the model developed in this study is able to evaluate the desirability of each travel based on not only cost and time but based on the other citeria if the data is available. Therefore, the power of the model is revealed when it is implemented on a public online or mobile application.

Having timely statistics and information from existing uses is a requirement for proper management of natural and urban areas. Considering the extensive changes in land use and the need for managers and planners to be aware of the changes that have occurred in order to make policies and find solutions to solve the existing problems, it seems necessary to reveal the changes in order to determine their time trends. Land use is one of the vital aspects in managing natural resources and evaluating environmental changes. The main goal of this research was to use remote sensing techniques along with GIS and Landsat TM and ETM+ satellite data in the period from 1975 to 2021 to detect changes in land use in Kerman region.

Classification of land use classes, analytical methods and changes were implemented using ENVI 5.3 and ArcGIS 10.3 software. The first step in this study included transferring the coordinates of sample points on satellite images to different land use classes. These points along with the objective data collected from field visits and unsupervised classification maps that show the spectral characteristics of the ground surface using different applications were used as training points for analysis.

The findings of this research after creating maps using Land and detailed analysis shows important changes in the way land is used during the studied period. In particular, urban and residential areas have increased from 1184 hectares to 2160 hectares from 1975 to 2020, which indicates a significant and sustainable growth. This rapid growth of urbanization has happened especially from 1995 to 2015 and has increased by 60% from 975.6 hectares to 2478 hectares. Along with the increase of residential areas, the studied area has also seen a significant increase in dry and unused lands. In the 2021 land use map, about 18% of the areas are classified as unused land. Considering the environmental conditions of the region, such changes may cause serious risks to the people of the region. It is necessary to emphasize that the environmental effects of these changes in land use should be considered. Climatic conditions and higher vulnerability compared to other areas require comprehensive and planned changes in land use. These changes have not only changed the landscape of the region, but may also endanger the ecological balance and sustainability of the region.

Land use maps are essential tools for national and regional development planning. They provide information that shows the current state of land use, allows comparison of capabilities and potential, and designs measures to meet current and future needs. Through the analysis of land use changes in Kerman during the last 45 years, this study shows the impact of urbanization and population growth on the landscape of the region. Its results emphasize that responsible and sustainable changes in land management are necessary to reduce harmful environmental impacts and promote long-term growth. In addition, it emphasizes the great importance of remote sensing technologies and GIS in monitoring and managing changes in land use and provides valuable data for informed decisions by authorities and related organizations.

The alteration in the landform pattern leads to changes in other landscape parameters such as soil, geology, and vegetation. Hence, the study of landforms and the investigation of factors that influence their formation and evolution are significant scientific disciplines in the fields of geomorphology and pedology. Climatic conditions with influencing on the geological processes, in conjunction with topographic features and the characteristics of parent materials, play an important role in forming and transformation of landforms.

In this research, the landforms of the study area were delineated using a digital elevation model with a resolution of 10 meters and the SAGA software based on the topographic position index. Subsequently, in two landforms, namely the plain and the open slope, which exhibited the highest degree of repeatability in the region, excavation and sampling were conducted, and the physicochemical properties and parameters such as parent material, elevation, and slope percentage were examined for each profile.

The studies indicated that in the plain landform the variation of slope is low and its maximum is three percent. This subject proves the transportation of parent materials from higher geomorphic surfaces accumulated in lower areas, resulting in the formation of this specific type of landform which is suitable for agriculture. Moreover, in the open slope landform with relatively high slope variations, erodible marl parent materials underwent alterations and transformations over time, leading to the development of distinct geomorphic surfaces. In this landform, the percentage of clay is reduced with depth, except for the profiles that have irregular patterns. Because of almost high slope, the soil depth of this landform is low. The reason for this low depth of soil is that the position of slope has the most important effect on the soil depth. Therefore, the positions on the top of the slope have low depth soils than the position on the bottom of the slope.

Topography has been known as a major factor in evolution and development of soils. The studied landform of this research showed that not only topography has a direct role in pedogenic processes but also parent materials are very importance in change of the processes and in landform construction. In this research, the landforms of the study area were separated based on topographic position index. In topographic position index, height and slope are used as the basic parameters for analyzing and classification of the landforms. Surveying the physical and chemical characteristics as well as assessing the parent material, height and slope percentage of the dug-up profiles in the more consequential landforms of the study area revealed that parent materials and slope have effective roles in creation of the landform. However, in the study area because of the arid and semi-arid conditions of the region and reduction of aeration and washing, the role of slope in creation of landforms was stronger than the role of parent materials. Thus, simultaneous study of the role of slope and parent materials in the regions with high aeration and active processes in geomorphic surface changes may create a broad context of research on creation of landforms.

Today, it is very important to know the quantitative and qualitative characteristics of changes in environmental planning, land use and sustainable development. Currently, the use of vegetation maps is one of the important elements in generating information for macro and micro planning. In general, there are various methods for collecting data, including astronomical observations, photogrammetry, mapping and remote sensing. Remote sensing is one of the data collection methods in which it has the least amount of direct contact with the objects and features being measured and unlike other methods in which human factors play a role in collecting and interpreting terrestrial data, in remote sensing method the task of collecting information will be the responsibility of the sensors. Due to the over-exploitation of natural resources, the landscape is constantly changing and monitoring these changes as well as updating maps is costly and time consuming, so many developed countries now have to prepare maps in Different levels use satellite data.

In this research, we will how remote sensing satellite images can be used to prepare maps of vegetations. The use of plant coating maps is one of the important pillars in the production of information for macro planning. The aim of this research is to produce a map of natural and cultivated vegetation as well as irrigated lands and gardens using remote sensing technology and geographic information system. For this purpose, satellite images and ground surveys were used as input data and these data were analyzed using different methods of classification. As a case study the south of Zanjan city was selected for preparing of land use map. The Sentinel-2 satellite images of 2018 were used. Several software such as Envi, ArcGIS, and SAGAGIS were used. The best classification method was selected using ArcGIS software. To check the functionality and efficiency of the data, normalized vegetation index (NDVI) images were calculated with a spatial accuracy of 15 meters and one NDVI image was considered for each month. In total, 12 images are selected and converted into a triple band by layer stacking.

Data validation is based on Google Earth and the precision of the vegetation of the study area was studied using classification methods such as Maximum Likelihood, Minimum Distance, Support Vector Machine, Neural Network and Random Forest. In each classification method, the Kapa coefficient has been examined by using the commission and omission error matrix and the overall accuracy of the map. It was determined by comparing all the methods and evaluating the obtained results, Random Forest Algorithm method with overall accuracy of 96.51% and kappa coefficient of 0.8181 was chosen as the best method of vegetation classification in the study area.

The results of this research indicated that in the random forest algorithm, the probability of each pixel belonging to each class is determined with high accuracy. The ability to determine the importance of features in the classification that can be used to select the optimal feature space is one of the most important advantages of this method. Also, the ability to classify noisy data, which can be used to increase and refine training samples, no need to select or reduce bands when using multispectral images, are other advantages of the random forest algorithm. In this way, the simplicity and comprehensible structure of this algorithm, along with its technical advantages, has made it highly flexible and made it very easy to combine with other methods.

Severe earthquakes cause a lot of human and financial damage, which was prevented from occurring after the accident with quick and timely relief. One of the important problems in this field is the optimal allocation of injured to medical centers, and these problems have a dynamic and complex nature and cannot be solved by simple methods. The use of geographic information systems (GIS) along with optimization and simulation methods makes it possible to find a method for optimally allocating injured to medical centers. The problem of allocation of earthquake-related injured to medical centers is in the category of capacity allocation problems, and in this type of problems, with the increase in the number of demand points and service centers, the complexity and volume of the problem calculations increases exponentially, so in many cases, it is not It is possible to use direct and definite search methods in solving this type of problems, and appropriate innovative methods should be used to solve them optimally.On the other hand, because data plays an important role in allocating injured to medical centers, it is possible to move towards a better and simpler solution by integrating and combining the spatial information system with optimization methods.

In this study, it is assumed that in the event of an earthquake in a number of building blocks, a number of people living in them will be injured and need help, and the locations of this injured population are the demand points. In this way, these injured should be sent to medical centers, each of which has the ability to provide services to a certain number of these injured. The next parameter is the number of medical centers and their capacity to provide medical services. In this research, the capacity of medical centers is assumed to be less than the number of injured people. Finally, by optimizing the objective function, a ratio of injured people at different points that should be accepted by existing or new treatment centers is calculated using a nested genetic algorithm. The output of the genetic algorithm that determines the location of the new centers is combined with the existing information, which is the location of the existing centers, and then it is used as the input parameters of the spatially guided tabu search (SGTS) algorithm to determine the best allocation.

In order to assess the accuracy of the genetic algorithm and SGTS method, the standard deviation, accuracy, and processing time have been evaluated, which SGTS method has performed better in all three assessment. The results show that the standard deviation ratio of the proposed method compared to the genetic algorithm is 0.12, and the average accuracy of SGTS method has improved by 18% on average compared to the genetic algorithm. Also, the SGTS method performed calculations 7% faster.

By comparing the processing time to optimally solve the problem of earthquake-related injured allocation to medical centers, it can be concluded that SGTS method can reach convergence in a shorter period of time. Therefore, creating a selection list using the structure provided based on spatial analysis can be effective in this field. The SGTS method is more accurate than the genetic algorithm, and the results obtained from this method are robust. For solving the capacitated location-allocation problem, if the objective of optimization is locating and allocating, then combined algorithms are recommended for optimally solving both. Although this study has succeeded to propose a combination method for the optimal solution of the capacitated location-allocation problem, it is recommended to conduct the combination of meta-heuristic methods and compare the results with the proposed method.

Geospatial Information Systems provide required science and tools for various transactions related to spatial data. Such an ability caused use of Geospatial Information Systems in several fields. One of these fields is mine exploration which can be known as highly dependent field to the geology. On the other hand, the traditional methods are not suitable for requirements of mine exploration. Thus, the new methods are utilized more and more for mining as well as mine exploration. Artificial intelligence is one of the new sciences transforming human life. Artificial Neural Networks as a long-standing method of artificial intelligence have found many applications in mine exploration. The goal of this research is producing mineral potential map using artificial neural networks.  

Artificial neural network is a classifier method in essence. This method acts like a black box which is trained at first, then it is capable of classifying the new data. In this research the data captured from exploration studies of porphyry copper deposit located in Yazd, Iran is used for producing mineral potential map. The mineral potential map is essential and very important for mining activities. The data is entered to a feedforward back propagation artificial neural network. The artificial neural network is used in two manners. The first manner is the usual one: The artificial neural network is trained by the aid of boreholes data and then it is used to forecast the potential of copper at every cell of the study area. In the second manner after training the network, its inner weights are extracted. These weights show the amount of importance that the neurons of the network have been considered for the input criteria. These weights are entered into the index overlay method. Afterwards, the criteria maps are combined by index overlay method and the mineral potential map is produced.

In this research more than testing the power of artificial neural networks in producing mineral potential map, testing the accuracy of its inner weights to be used in another method is aimed. At the training phase the values of criteria maps (essentially produced by exploration studies) at the positions of boreholes are entered into the artificial neural network and the network should forecast the potential of copper at that position qualitatively, while the true values are known from the data of boreholes. Then, the trained network forecast the potential of copper at every position of the deposit. The results revealed that the accuracy of artificial neural network when ignoring one of the non-efficient criteria can be reached up to 100 percent. However, the accuracy of index overlay method using the weights extracted from the artificial neural network is about 70 percent at maximum.

The results of this study revealed again the power of artificial neural networks in classification and combination of spatial data. Despite, the unique result of this research is that the inner weights of artificial neural network have the maximum performance in their network and using them for weighting and combining data by another method would not be useful. However, these weights can illustrate the order of importance of data.

At the present time, the industry of space systems design, manufacture and launch has fallen out of favor with governments and numerous private sector representatives around the world are competing with each other for a greater share of this thriving business. The tendency from large single-satellites, high life-cycles in high-altitude orbits to high numbered constellations consisting of small satellites with low life-cycles and in low-altitude orbits is one consequence of this change. Space industries are increasingly keen to deploy small and low-cost satellites which demand for low-cost design. Technological advances in the design and manufacture of each satellite subsystem have accelerated this process and it has made the new generation of satellites superior not only in size but also in terms of performance. Minimizing multiple cycles in the design process and replacing cyclic optimization methods with straightforward ones can help improve this process. Rapid sizing techniques are well-known in aircraft industries as they allow designers to quickly prepare a ball-park design for their intended aircraft. In this research, we propose a similar approach, for Very High Resolution Passive Scan Agile Earth Observation Satellites that allows designers to become aware of the design different boundaries. The key is to prepare a 2/D space which describes any specific mission-leg with respect to the key configuration parameters. Such a design tool exhibits critical mission phases and their relationship to the key technological factors. In this approach, a designer can quickly decide upon technological barriers that might influence the Research, Development, Test and Evaluation (RDT&E) phases of the design and/or negotiate with stakeholders on any changes to the satellite mission. As total life-cycle cost is normally influenced by decisions made during RDT&E phase, it is expected that this method play an essential role to keep the overall cost down. Such rapid-sizing technique allows designers to do more trade-studies. This research has been concentrated on three main issues: (1) Existence of a design space for RS-satellites (2) The parametric characteristics and influential parameters that form such space. One suitable case-study have been discussed to support the proposed methodology. The maximum mass of VHR-PS-AEOS is largely influenced by its overall configuration, and its minimum mass is also influenced by the altitude reduction rate during the satellite's operational lifetime. The dimensions of the payload and its placement inside the structure to provide the required agility are critical requirements for determining the overall dimensions of the satellite and as a result its surface and volume. This research has been concentrated on three main issues: (1) Existence of a design space for RS-satellites (2) The parametric characteristics and influential parameters that form such space. One suitable case-study have been discussed to support the proposed methodology. The maximum mass of VHR-PS-AEOS is largely influenced by its overall configuration, and its minimum mass is also influenced by the altitude reduction rate during the satellite's operational lifetime. The dimensions of the payload and its placement inside the structure to provide the required agility are critical requirements for determining the overall dimensions of the satellite and as a result its surface and volume.