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One of the most complex issues of urban planning is informal settlement which is a multidimensional phenomenon with multiple indices. The spatial growth of informal settlements is affected by complex internal and external drivers. Combined use of GIS and agent based model as an approach of this research might be an adequate strategy for modeling these process. The purpose of this study is modeling informal settlement growth of the phenomenon on a cadastral scale in vector GIS, using land parcel agents and decision maker agents. Two scenarios were considered for defining the neighborhood effects and results of modeling were investigated and were evaluated for these scenarios. In this model decision maker agents as moving agents include people who are working in the downtown core and those who are working in industrial suburb areas.They search the surroundings to find suitable land for housing. Land parcel agents as immobilizing agents calculate their total suitability using spatial factor based on the preferences of decision maker agents. The proposed model was implemented on one of the informal settlements districts in the city of Kashan, Isfahan province, Iran. Evaluating results with actual data indicated that the proposed model was able to predict the informal settlements growth with 86% overall accuracy. This study indicated that although the developed neighboring numbers are important to develop a land parcel on cadastral scale, but developed neighborhood areas have a greater role than their numbers. The findings of this research also indicate that developed parcels located within the radius of 120 meters of a target land parcel have different effects on the development of target parcel proportional to their distances.

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Rock mass characterization is one of the most important parameters affecting the underground mine design. This study deals with the prediction of rock mass quality using geo-statistical estimator in Anguran underground mine at the level +2740. For this, a database consisting of 427 Q-based rock mass quality data sets was developed during the development of mine drifts. Accordingly, data were analyzed and checked for normality, trend and anisotropy. Analysis on Q-data showed that: 1- they do not have a normal distribution, 2- there are neither global nor local outliers in data 3- the data seem to exhibit a trend. In this study, the Universal Kriging was used due to existing trend in datasets. Taking into consideration five estimation error evaluation criteria, the best Variogram model was selected among three models: exponential, spherical and Gaussian. The results showed that spherical variogram model provides the best fit to the data's spatial structure. Cross validation showed high accuracy level for performance of geo-statistical estimator. Accordingly, the rock mass quality map for the area under study built in ArcGIS environment. The analysis results of final rock mass quality map revealed that about 53% of under study area has poor to extremely poor rock mass condition, 8% has fair and 39% has good to extremely good rock mass condition.

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The exploration of hydrocarbon resources as a process is very complex and costly. In this process multiple factors of geology, geochemistry and geophysics are considered and combined together. Designing the best route to take seismic data and determine the best location for drilling exploration wells is extremely important. Since improper or careless determine the selection of location is time consuming and expensive during the operation. The aim of this study was to identify possible areas for oil and gas in the map of 1: 250,000 Ahvaz with 20 oil fields using adaptive inference systems neuro - fuzzy (ANFIS) and geographic information systems (GIS). For this purpose, 17 maps of factors including: the lowest and highest value (total organic carbon (TOC), potential for the production of hydrocarbons (PP), peak Tmax, the production index (PI), oxygen index (OI), hydrogen index (HI)) and the proximity to areas of high bouguer gravity anomaly, anticline axis and faults, map the topography and curvature of the yield curve Asmari subsurface were created by GIS functions. For combined factor map, the adaptive inference systems neuro - fuzzy (ANFIS) that is data-driven methods were used. The results of test data showed that the model with a R = 0.839 ,RMSE=0.0339 and the Kappa=0.859 was able to accurately predict the oil fields, but fields such as Shaver and Sepehr have not been identified and Also some areas were mistakenly classified oil fields.

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Ambient intelligent services are a set of services that are inside a specific zone provide seriveces for the users inside that area. Streets are one of the most important ambient intelligence environments in the cities. A smart street refers to a street which is able to provide its users relevant and proper services according to their conditions. Information services are one type of ambient intelligent services. Commercial messages of shopping centers, traffic statistics of various urban districts and police announcements from mobile or static urban stations are all important examples of urban information services. Since most service users, such as vehicle drivers, are constrained to move only on a network, service sites should be defined on the network as well. Determining the service provision region especially on the streets network and particularly for vehicles is an important issue that has not yet been studied in ambient intelligence. Utilizing the Network Voronoi Diagram (NVD) is one of the spatial analyses in Location Based Services, especially in the streets network; since adjacency to service centers should be considered in regard with the distance on the network. In the present study, a method for providing vehicles on the streets network with information services has been proposed. In this method, factors such as the adjacency of vehicles to services sites, servicing time and vehicle movement quality direction are taken into account. The obtained results from implementing the proposed method in one district of Tehran indicate 68 percent  satisfaction with the system performance in utilizing network spatial analyses.

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Positioning in road network environment requires a process with which can be able to match the raw coordinates, obtained from the positioning sensor(s), to the links of the network. Such matching process is necessary for two obvious reasons. First, positional data is not definite, and second, map coordinates are not absolute. Hence, there is a need for a process, known as map-matching, to reconcile two groups of coordinates. Accordingly, to provide some location-based services in network areas, performing a map-matching process seems inevitable. In this paper, after discussing several types of geometric map-matching methods, which is the most basic form of map-matching, a weighted-base map-matching algorithm is developed.The participating parameters’ weights are optimized experimentally. The algorithm takes three parameters as input: ‘Distance’, ‘Heading Convergency’, and ‘Relational Position’. Four types of modelling for ‘Relational Position’ are presented. The most effective type is then recognized after executing tests on algorithm performance. Also, comparing the performance of the suggested map-matching algorithm to the map-matching algorithms of the same complexity developed in other studies shows that the suggested algorithm is more efficient. This paper's suggested algorithm provided 95.5 percent of true link selection during the performance assessment.

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Abstract

Difference between geometrical data (dimensions and area) of parcels in cadastral title and cadastral map are motive for geometrical map distortions after geo-referencing. These distortions occur due to traditional surveying methods or environmental effects in maintenance of maps. Because of legal and financial complexities of cadastral maps compared with other map types, conflict between map and title makes social and legal problems. Therefore objective of this paper is recommend a method to decrease distortions of cadastral map through matching geometrical data of parcels in cadastral map with cadastral documents. ­­­Recommended solution for this objective is using finite element transformation method. In this method control points constraint parcels dimensions and area constraint simultaneously convenient weights are added to continuity constraint and solved as a parametric listsquare .This process is implemented for two hardcopy maps and their title data then residuals decreased to a very low level. tests shows that RMSE of dimension improved from 65 cm to 1.2 cm, RMSE of area improved from 8.10 square meter to 0.0003 square meter and control points RMSE reduced from 1.12 meter to 0.65 meter.

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By developing wireless mobile sensors, volunteered geographic information production and social co-operating increase, urban monitoring witnessed a considerable change. On the other hand, air pollution become one of the most important environmental challenge of Tehran and tackling it is not possible unless knowing the pollutants and their sources. Accurate investigation of spatial distribution of pollutants in cities requires development of spatial variability models of pollutants. But, high cost of developing and keeping of Air Quality Monitoring Network (AQMN) is one of the biggest problems in front of councils in developing such monitoring stations in cities. In this research, in addition to designing and building a mobile carbon monoxide monitoring system and calibrating it in the lab, data of pollutants concentration in 4 time periods in district 6 of Tehran was collected. Then by using land use regression method the spatial distribution model of this pollutant for the investigated area in each time period was determined. To reach this achievement, different environmental factors such as land use, Roads, elevation and traffic as independent factors and the measured pollutant concentration as dependent factor were applied to the mentioned model. Finally, this research led to provide carbon monoxide pollution maps for the investigated area, which are useful in finding high risk locations. Evaluating the statistical parameters show that in estimating pollutant concentration of different urban districts, the suggested method is practical to some extends.

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Traffic elements are considered as important elemnts in traffic management. In Iran, overcrowding of automobiles in the cities led the traffic elemnts to be more vehicle-driven rather than pedestrian-oriented  hence safety and well-being of pedestrains took less into consideration. Pedestrian bridge is one of the most important traffic elements which is strongly effects the security of pedestrians passing across the streets. Finding the best locations to install pedestrian bridges is normally done based on less comprehend and analytic prepared regulations. This causes low efficiency of many bridges and people choose the street itself instead of these safe passages. In this research alongside usage of spatial processing tools, an agent-based model is developed for assessing the pedestrians behaviors while going across the street. Agent-based model provides the possibility of taking the decision making process and its efficient situations into account which results in more reliable consequences. The developed model was used to evaluate four existent pedestrian bridges in Pasdaran Blvd, Shiraz. The results showed that performance of the least used bridge can be improved up to 27% by a 150 meters displacement. Then, the model was deployed to find two best places for a new bridge. The model anticipate that if a new bridge is constructed in each of new suggested locations, the pedestrians will use it more often than the existing four pedestrian bridges.

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The megapolis areas are new types of urban settlements created in recent decades along with rapid urbanization. These areas constructed by clusters of small and large urban patches with various growth patterns. Spatial characteristics of urban patches are affected by some driving forces such as closeness to cities Central Business Center CBD and transportation network. The Cellular automata as a most common model for simulating urban growth, is unable in modeling spatial configuration of urban patches because of bottom up procedure and despite of high simulation power at cell level, CA has weaker performance in patch level. So in this study a method is presented for simulation of urban patches growth that is integrated with Logistic CA to modeling urban growth. In this method, on the one hand the growth potential map derived using logistic regression and on the other hand size and growth type of patch in each location is derived using integration of driving forces of growth patterns of urban patches. Finally according to proposed framework, a patch is constructed around selected cell and urban growth map will be prepared. The proposed model is implemented in the Tehran’s megalopolis area in 1379-1385-1391-1397 periods. The overall accuracy and FOM of results is equal to 91/01 and 37/96, respectively that are better than logistic CA model. Also the results of validation of produces urban growth map by using spatial metrics reviled high precision of methodology in simulation of spatial configuration of urban pattern.

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Cultivation of agricultural land in the basin Zarineh has changed over the years and as one of the main reasons for reducing the inflow to Lake Urmia has been raised. Due to the possible development, Estimation of prospects of changes in the basin can play in key role in taking  effective decisions and provide guidelines for dealing with the environmental crisis Lake Urmia is facing with. In this study has been tried to extract and assess the historical changes in land use in the Zarineh  basin using satellite images and a projection of the future land use changes has been provided. In this regard, a combined of CA-MARKOV method was used. Results show that between 2000 and 2013, on average, most of changes lean toward  increase in land use, such as irrigated agriculture (40%) and gardens (57%) and also to reduce pasture land (5%) and dryland farming (10%). This trend has also been observing  for horizon of year 2020.

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Large amount of users’ trajectories, is an emerging source of inexpensive data that can be used to provide an opportunity to present route recommendation service to the unfamiliar users within the area. In this study, with the aim of finding the optimal route, we first extract both local users and local road segments data sets by ranking them via HITS algorithm. In this model, a hub is a user who many time has crossed many road segments of a region, and an authority is a road segment that has been crossed by many users. Therefore, users’ travel experiences (hub scores) and the interests of road segments (authority scores) have a mutual reinforcement relation. We also propose a novel approach in which the basic unit of routing is separate road segment instead of GPS trajectory segment. Moreover, to provide the approximate routing, we create a local graph. The center of the local road segments are considered as nodes and are based on local streets sequence arrange the pieces obtained from the trajectory of the user as edges of local graph. According to this graph, two steps of routing are used to obtain the optimal path. Then using Dijkstra's algorithm on the main road network and obtained an approximate route, shortest route between two local road segments based on this graph is used to obtain the optimal route. To implement and test, used data, from the trajectories of moving users in Tehran, has been gathered for 3 months on daily basis. To evaluate performance of the two-step routing, we experimentally compared the travel time in proposed route to Dijkstra’s shortest path for different lengths and users with different levels of regional knowledge. The travel time in the proposed method was decreased 60 percent  compare to shortest route.

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Daily growth of unmanned aerial systems (UAS) technology in areas related to geospatial sciences and location-based services makes it easy to employ them in digital photogrammetry, enironment monitoring, infrastructures, sensitive constructions and medical support and aid delivery tasks. In this area, fast and efficient development in UAS path planning can enhance the productivity, efficiency and quality of these missions. In this research, an efficient UAS path planning strategy is proposed based on the imperialist competitive optimization algorithm, which can improve the productivity and autonomy of different missions in new challenges such as medical support and aid delivery scenarios. For this purpose, first, with respect to the objectives and restrictions of the scenario, a single objective constrained optimization model is designed. Then, to intensify the exploration and exploitation capabilities of imperialist competitive algorithm, the assimilation step is integrated with gravity physics. In simulation phase, the results obtained by proposed method has been compared to the results of the other variants of this method with respect to the quality, standard deviation, running time, success rate and quality of the computed paths. The assessment in multiple simulations verifies the efficiency and robustness of the proposed strategy and quality of the obtained paths in studied scenario.

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In a multi-modal multi-objective route planning problem, the main purpose is finding an optimal route between the origin and destination, which is a combination of multi-transportation modes, pairs by considering multi-fitness function. Most of multi-objective problems are solved by assigning a weight to each objective function and using a linear averaging of the objectives as a distinct objective function. These methods have some weaknesses such as inability in searching the problem space and a need to normalize the objective functions. Therefore, in this paper, a non-dominated sorting genetic algorithm (NSGA-II) has been used to solve the multi-modal multi-objective routing problem. This algorithm proposes a set of non-dominated routes that has no absolute superiority to each other. Finally, the optimal route was determined using TOPSIS method from this set. The intended objective functions in this research are the lowest number of changes in transportation means, fare and time during the path. Moreover, five transportation modes including subway, taxi, bus, BRT, and walking transportation modes have been considered as means of transportation inside the mentioned network. This algorithm was implemented in a part of Tehran transportation network and results showed that the proposed NSGA-II algorithm proposed a better route in 89% and 87% of the routing cases than those of the genetic and the simulated annealing algorithms respectively.

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Cognition of travel mode and travel demand is of prime importance to transportation communities and agencies in every country. If the precise transportation modes of individual users are recognized, a more realistic travel demand can be considered. Also, in location-based service, the knowledge of a traveler’s transportation mode is applied to send targeted and customized informative advertisements. This study examines the feasibility of using a neuro-fuzzy inference system to automatically detect the mode of transportation from GPS data collected by GPS-enabled mobile phones. To achieve this, the knowledge was extracted in the form of fuzzy rules from the data and, then, the rules are being used for determination of transportation’s mode. For this purpose, the model was examined in two cases. In the first case, all GPS data from mobile devices were used, while in the second case the critical point algorithm was exercised. In addition to reducing the size of required GPS datasets, the critical point algorithm decreases data collection cost and saving mobile phone resources such as its battery life. The results showed that the suggested model have the capability of detecting a transportation  mode with 94/1 percent accuracy in case of using all GPS data and 95.5 percent accuracy in case of using critical points.

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Quality and quantity of vegetation land cover is considered as one of the important aspects of environment. Detection of trends in natural phenomena such as vegetation, requires long-term studies, more than lifetime of a satellite. On the other hand, combining data from different sensors could lead to formation of false changes. One of the main causes of false changes is different spectral sensitivity functions (SRFs), among sensors under study. In this regard, the impact of these factors should be eliminated or reduced as much as possible by a procedure named relative calibration which is the main goal of this research. There are similarities between Landsat satellites series and SPOT-5 with Sentinel-2 in many aspects, so MSI (the Sentinel-2’s sensor) has capacity for data continuity. In this study, by incorporating polynomial equations, Landsat sensors (OLI, ETM +, ETM) and SPOT-5 were calibrated relative to MSI. The combination of radiative transfer models; PROSPECT-4 for leaf and 4SAIL for canopy, were used to simulate 50000 top of canopy synthetic spectral signatures and then soil effect was combined with them using linear spectral mixture model. After all, 150000 signatures were simulated. These spectral signatures were transformed to equivalent reflectance values (Blue, Red, NIR and SWIR) and spectral indices (NDVI, EVI and NDWI). 80%   of spectral signatures were selected randomly for solving relative calibration models. Also, for validation purpose, remained simulated (20%) and 38   top of canopy measured spectral signatures were used. According to the results, linear equation can model the difference (caused by SRF) between MSI and others quite well and there is no need for more complicated equations. In general, results of this research show high and acceptable correlation for all reflectance bands and indices. It is more necessary to perform a relative calibration pre-processing step for EVI time series. Amongst reflectance bands, NIR has the highest continuity

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In this study, a method for unsupervised change detection in multi-temporal SAR images has been presented based on integrating clustering and active contour model. In this method, texture information is extracted by using Gabor filter in different scales and directions. KPCA transformation is also applied to reduce the dependency between the extracted features and image information. Moreover, Discrete Wavelet Transformation (DWT) and Gustafson-Kessel clustering (GKC) methods are used respectively to generate the difference image and the initial contour for the active contour model. In the final step, the region-based non-parametric active contour model is used for producing the change image containing changed and unchanged regions. For performance evaluation of the proposed method, two sets of high resolution multi-temporal TerraSAR-X images are considered. Experimental results of unsupervised change detection method show that,  the total error rate of the proposed approach for the first data set are decreased respectively to 4.95%, 3.30% and 3.34% compared to that of the  Chan-Vese, MRF and EMMRF methods and for the second data set, the total error rate of the proposed method are decreased to 2.56%, 1.86% and 1.87 As well. Moreover, the results showed that the use of GKC method leads to production of the initial curve with minimal convergence time for the active contour model. Also, the use of active contour model improves the accuracy of change map creation using a repititive process.

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Population growth, urbanization and immigration from rural areas to cities results in increasing expansion of
the cities. The adequate urban development requires proper and accurate planning, such those facilities for the
new areas that are provided and environmental impacts are lessened. Computer based simulation and
prediction of urban growth can be assumed as a good start for urban planning. In this research, a new agent
base simulation of urban growth is developed, in which the communication and decision of agents are imitated
from the Particle Swarm Optimization (PSO) algorithm. The model is tested on the urban growth of Zanjan
city- Iran between 2005 and 2015. In this model, land developers are classified into three groups of agents
according to their income level. These agents search the environment and find proper lands for development
according to their priorities and conditions. The output of the model is 74% similar to the reality according to
the Kappa index. This and other results show that the model can predict the expansion of the city adequately.
Moreover, the comparison made shows that modeling of the relations and communications between agents
similar to PSO can slightly improve the quality of the model. The results showed the adequacy of the proposed
agent-based modelling for the simulation of urban growth. To have a more accurate model, it is recommended
to model the behavior of the agents with more details and also to consider the competition between agents.

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Although, the distinction between the land cover classes was increased in large feature space of remote sensing images, but
the low number of training data prevent this. In order to solve this problem, ensemble classification methods can be used
instead of individual classifiers. In this paper, a new method for ensemble support vector machine was proposed called
“Support Vector Random Machines (SVRMs(”. In proposed method, bootstrap was produced using modification of
training data and feature space. Simultaneous boosting SVM was used for basic classifiers. Then, classification map was
resulted using SVM fusion of basic classifier. Hyperspectral and Polarimetric SAR data was chosen for evaluation
performance of the SVRMs. Experiments were evaluated from three different points of view: First, evaluation against other
ensemble SVM methods; second, evaluation against various feature selection methods in classification and third,
evaluation against the various basic and new classification methods. As the results, proposed method is 16% better than the
individual SVM classifier in hyperspectral data and this is 10% in PolSAR data. Also, the classification results of SVRMs
in various classes compared to other SVM ensemble method were improved. The results reported from the proposed
method compared to the other feature selection method (Genetic Algorithm) has the effectual performance in classification.
The results show that the proposed method presents a better performance compared to the basic classification methods
(maximum likelihood and wishart) and advanced classification (random forest and neural network).

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Up-to-date digital maps play a significant role as required data in various areas such as navigation, tourism, traffic control, urban and interurban fleet management, ITS, web-based map services and location-based services. Nowadays, VGI is known as an important resource to generate digital maps. In the last few years, the use of these resources to produce low cost, up-to-date, and reliable maps have been considered. Previous studies have utilized static methods for automatic road extraction from volunteered trajectory data. However, they have not considered the continuous changes of the road network which are reported dynamically by the volunteered trajectory data of users. So, due to their static nature they are not efficient to update road maps dynamically. Considering the fact that crawler-agents are capable of sensing and reacting changes in the environment it is possible to dynamically update road maps based on the latest changes presented by the trajectory data. The key idea of this research is to design and develop crawler-agents that dynamically search and update road maps from volunteered trajectory data. In this article, inspired by the continues monitoring of the web by web Crawler-Agents, a dynamic method is presented to updating road maps with considering the collected participatory data that has been gathered and recorded by the GPS sensors on the mobile device of users. To do so, various group of agents that move on trajectory data to extract road map are designed and developed. EM clustering is used to estimation of the network nodes. Also, a heuristic method is developed to link the nodes which are related to each other. Furthermore, an efficient approach is presented to extraction of road networks e by applying three dimensional trajectory data in the case of road’s challenge structure.
 

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Population growth, limited usable agricultural lands, rapid changes in societies and land uses development, highlights the importance of proper planning for land use and cropping pattern optimization. The present study was carried out for agricultural crops cultivation planning using water requirement estimation and spatial optimization methods. The model was programmed into three sub-models. The objective of the first sub-model, was assessingthe crops water requirement. For this purpose, the meteorological observations collected from 16 high quality meteorological sites for a 20- year period (1985-2005). Objective of the second sub-model, was determining the land suitability for agricultural crops. To achieve this goal six criteria were considered. The criteria classified into two groups including climatic parameters and land characteristics. Finally objective of the third sub-model was cropping pattern Optimization by considering the maximum benefit and minimum consumable water in study region. In order to reach to this target, linear programming was used. Result of linear programming showed that due to water shortages in the region, Simultaneous cultivation of all parcel is not possible and about 40 percent of the parcel to remained fallow, in each year separately. Also in the optimal state surface of products such as potato, alfalfa and sunflower due to high water consumption, exposure of the
growing season in spring and summer and having lower gross margin were removed from the cropping pattern and wheat and barley crops constituted area under cultivation more than other products.