JOURNAL OF GEOMATICS SCIENCE AND TECHNOLOGY
Year:2016 | Volume:5 | Issue:4|Papers Count:21

Frequency decomposition of the Earth gravitational potential in terms of spherical/ellipsoidal harmonics has been of the significant matter for a wide range of applications such as the geodetic, oceanographic, and geophysical purposes. These days, thanks to the notable advancement in the field of satellite altimetry, monitoring the sea level on the global scale has been realized in practice. Meanwhile, the gravity information may be derived from these valuable data if the accurate mean dynamic topography has been obtainable. In this respect, the mean dynamic topography can be determined via the oceanic or geodetic approaches. In the oceanic manners, the mean dynamic topography is derived using the oceanic information such as salinity, temperature, and surficial currents; while according to the geodetic methods one can obtain the mean dynamic topography by integration of satellite altimetry measurements and global geopotential models. In this contribution, we aim at assessing the feasibility of improving the pre-existing geopotential models by means of satellite altimetry observations. To this end, the sea surface topography is estimated using the geopotential and mean sea level models in a constrained least squares sense. As such, we can arrive at the Gauss-Listing geoid over the sea areas derived from the computed sea surface topography and the known mean sea level values. The Bruns formula is then implemented to reduce the resultant geoid into the gravitational potential values over the oceans on the surface of the reference ellipsoid. On the other hand, the gravitational potential values over the continental regions are obtained on the surface of the reference ellipsoid via the geopotential model of interest, once the topographic bias corrections have been considered. Lastly, a new point-wise geopotential model in terms of spherical harmonics is developed through application of the spherical harmonic analysis to the worldwide gravitational potential. As the case study, the presented methodology has been evaluated so as to improve the two global geopotential models, namely EGM2008 and go_cons_gcf_2_dir, up to the degree and order 90. Accordingly, the DTU10 mean sea level model, which has been derived from information of Topex/Poseidon, ERS1, ERS2, ENVISAT, Geosat, GFO, and Jason satellites, has been applied to the EGM2008 and go_cons_gcf_2_dir models in order to estimate the sea surface topography based on the proposed optimization solution. Consequently, the improved versions of the global geopotential models have been developed thanks to the application of the harmonic analysis to the gravitational potential values that have been attained over the sea and land areas on the surface of the reference ellipsoid. Based upon the numerical results of the assessment of the developed models at the first-order GPS/leveling points within the test areas in Iran and Finland, the capabilities of the proposed method in deriving enhanced geopotential models have been asserted. Moreover, the comparison of the consequential enhanced models with respect to the BGI gravity points have demonstrated the efficiently of the method throughout the world. As a whole, we have deduced that the presented method can be applicable to significantly improve an extensive range of the global geopotential models.

Height variation in different urban objects e.g. buildings and trees coincide to occurrence of shadows in aerial and satellite images. Areas casted by shadow, appear darker than neighbouring areas in the image and it makes an unwanted contrast to the other brighter areas. This phenomenon attenuate different expectations from remote sensing data. In particular shadow areas ruins the result of automatic image matching algorithm and in land cover classification cause the misclassified pixels.Detection of overshadow areas is the primary step to deal with this problem. Different strategies have been used to detect shadow in remote sensing images. To name a few we can consider classification based methods, region-growing methods and different spectral indices. In classification based method, some ground truth from shadow areas are collected and supervised machine learning algorithms are used to classify shadow and non-shadow pixels. Region-growing algorithms use the high contrast between shadow and bright areas. Spectral indices are made by simple arithmetic equations between spectral bands.There is some deficiencies in the result of previous methods and strategies. In machine learning methods, existence of ground truth information is essential and somehow affect the results. Using region growing and spectral indices usually leads to addition of roads and vegetation to shadow areas. The result of all this methods are presented in pixel level, labelled shadow pixels. Wrongly detected shadow pixels appear as noises in classification map.In high resolution aerial and satellite imagery single pixels are not meaningful independently. This is the outcome of decreasing the ground sampling size of sensors versus natural objects on the earth. The solution to deal with this problem is to integrate similar neighbouring pixels which belong to the same ground object. Object-based image analysis (OBIA) is developed based on this idea, considers image object, created by segmenting the image, as processing unit. The power and possibilities of image objects are less discussed and considered in detecting shadow areas.In this paper we propose a new object-based framework for shadow detection which simultaneously benefits from OBIA, machine learning and spectral indices. Our proposed framework consists of four main steps. First step is the pre-processing of data. In this step spectral bands are pan-sharpened to enhance the spatial accuracy and the panchromatic band is segmented by eCognition Software. In the second step new spectral indices are proposed to overcome the weakness of existing indices in mixing roads and vegetation to shadow areas. To automate the process of detecting shadows from index values the Otsu thresholding algorithm is employed.Third step is object-based shadow detection. To detect shadow areas in object level, majority analysis of shadow pixels in each image object is considered. To solve the ambiguity between vegetated and shadow objects an extra condition is checked to confirm that an object belongs to shadow class. This condition uses the mean NDVI value of pixels in each image object. Finally in the fourth step evaluation of produced map is obtained using completeness, correctness and F-measure. In this step the result of shadow detection using spectral indices, proposed index, machine learning and proposed method are compared and analysed.GeoEye-1 satellite data comprised 4 spectral bands over Qom city in Iran is used in the experiment.800 shadow objects are selected manually to evaluate the result. Correctness, completeness and F-measure obtained from confusion matrix of shadow map are calculated to compare the results. The result of shadow detection by spectral indices and SVM and random forest classifiers have been compared to the result of proposed method. Result of our experiments demonstrates the superiority of proposed object-based over the pixel-based method respect to correctness and F-measure for different classifiers. The proposed method succeed to detect shadow area with 93% correctness and 92% Completeness It is also evident that object-based method have well behaviour on the edge of shadow areas and perfectly detect shadows.

Three-dimensional modeling of the human body has become one of the important research topics in computer graphics. This is due to the importance of virtual representation of the human body in applications such as animation, computer games, virtual fitting room and cases etc. This has been obtained in the context of software and hardware developments in computer graphics. In this regard, three-dimensional modeling of the human body with low cost, high quality and accessible to everyone without the complexity and the need for specific expertise for processing is of great importance.The aim of this paper is proposing a method for solving 3D human body modeling. Since the introduction of Kinect by Microsoft with features including low cost, no complexity, depth and color images production with a high frame rate and possibility of using in different lighting conditions, it could be a useful tool for our this purpose. But using the Kinect sensor for human body modeling confronts challenges such as raw data with low resolution and high noise, users movement during the scan, hidden areas and also a lack of accurate connection between depth and color data. In this regard, the idea of using the Kinect rotation motor in vertical angles in order to reduce the distance from the user to increase the quality of primary data was presented. The proposed non-rigid registration method was utilized for solving the problem of user instability during the scan. Also the sensor geometry calibration for accurate alignment of color and depth data was used.In this paper, the procedure for 3D human body reconstruction is as follow: at the first, person is stayed on a specified distance from the Kinect and is scanned in the eight stations at three vertical angles. Then, colored point cloud are achieved by aligning color and depth images and extracting user data from background. Then rigid registration between sequential data stations is performed automatically. In order to solve the problem of instability during the scans, non-rigid registration is done between data station pairs. Finally, a general mesh was generated from the final point cloud and texture mapping is done to produce a realistic 3D body model.The experimental results show that our rapid 3D human body modeling system has a high capability comparing to other similar systems. This system has a lower cost (less than 150 dollars), capacity of scanning in near distance without additional equipment such as rotation tables, and a higher quality of the final 3D model so that the details such as wrinkles and hair style is recognizable. The final 3D model generated from point cloud with about 4 mm density and 4mm noise thickness. Also the problem of low-quality in modeling of legs and shoes caused by a high movement during the scan, have been largely resolved. Therefore, we can generally say that the proposed method resolves the similar system’s weaknesses in data collection and processing steps. This makes our system proper for diverse applications and different environment.

The ionosphere is the ionized region of the atmosphere which is situated between 80 and 1200 km. Ionospheric delay is the major resource of error in GNSS positioning, Therefore knowledge of the ionospheric behavior is an important factor in this field. Total Electron Content (TEC) values may be considered as a key parameter to monitor the behavior of the ionospheric medium. Nowadays, continuous GNSS observations can provide an efficient tool to monitor timely ionospheric irregularities. Many scientists have investigated global ionospheric models on the basis of different observations data. For example, IGS Ionosphere Working Group produced daily TEC maps for user services from GNSS data. In this paper, we intend to utilize dual frequency GPS observations provided by Iranian Permanent GNSS Network (IPGN) to calculate TEC maps in Iran. For this purpose, data of 43 IPGN stations and about 180 IGS stations were processed with Bernese GPS software. This process was based on the use of spherical harmonics expansion up to degree and order 15 like the global one, to provide a model of TEC. In the meantime of using GPS data to calculate TEC maps, other resource of errors in GPS positioning such as satellite and receiver clock biases, tropospheric error and multipath error must be either removed, or at least significantly reduced. For this purpose, we used the geometry free linear combinations of pseudo ranges and carrier phases. For reducing the noise level of pseudo range observations we used the carrier phase smoothed pseudo range data as well. The processing method consists of several steps; code smoothing with phase observations, estimation of Differential Code Biases (DCBs), estimation of spherical harmonic coefficients and generation of TEC maps. Before code smoothing, the phase observations were pre- processed to remove the cycle slips. The used model assumes that the whole free electrons are concentrated on a thin spherical layer to an altitude varying between 250 and 450km. We chose the altitude equals to 450km in this paper. The obtained results show that the maximal TEC value measured over Iran is about 22 TECU, this value corresponds to the noon period (midday), where the sun is close to the zenith. The minimal TEC value varied around 5 TECU, it corresponds to the midnight period, and such values were obtained for the day of Jun 22, 2009. Iranian Ionosphere Model (IRIM) was created and compared with the different solutions delivered by the several IGS Ionosphere Associate Analysis Centers (IAACs) which are CODE, ESA, JPL and UPC. Despite different IAACs use various approaches, they provide TEC maps with resolution of 2 hours, 5o and 2.5o in UT, longitude and latitude respectively. In order to compare our obtained results with different IAACs TEC maps, we chose TEHN station from IPGN stations to generate and display TEC profiles. The differences between the various models are less than 6 TECU. The IRIM results had minimum differences with CODE TEC maps which both use spherical harmonics as their basic functions. The remained differences caused by the fact that when CODE TEC maps are estimated, the data from IPGN stations are not used. Calculated TEC values were thereafter applied to correct and improve the quality of the single frequency solutions in absolute and relative positioning modes. It is noted that ionosphere free (L3) solution results was considered as the reference solution. In absolute mode, we received the considerable improvements in horizontal and vertical components by using the IRIM instead of IGS models. In relative mode the comparison between the corrected L1 and L3 solutions showed that ignoring the ionospheric effects causes network contraction. Furthermore, the corrected L1 solution results using IRIM rather than IGS models were closer to the L3 solution results. Moreover, for baselines up to several hundreds of kilometers, deviations were better than 10cm in horizontal component.

Discussion about earthquake to reduce its casualties and damages is very important, especially in the Seismicity area like Iran that the occurrence of this natural phenomenon is seen annually. Iran has an approximate area of 1648000 square kilometers with geographical coordinates 25 to 40 degrees north latitude and 44 to 64 degrees east longitude that located in the middle of Alpine-Himalayan seismic belt. In this erea there are many active faults that their movement continues and the final balance has not been established. The occurrence of severe earthquakes as Buin Zahra earthquake (1962), Tabas (1978), Rudbar (1990), Bojnoord (1997), Bam (2003) and other numerous earthquakes prove this subject. While most natural disasters are out of human control, but it seems that Success in prediction of temporal and local of them can dramatically control damages and casualties. Earthquake occurrence in addition to changes of geometry and physics of the earth crust has many other effects. Some of its effects is in the ionosphere layer that are indicated as changes in the electrons values, ions density and electromagnetic field. Anomalies detection before earthquake is an important role for earthquake prediction. Each geophysical and geochemical parameter of the lithosphere, atmosphere and ionosphere layers that unusually changes before earthquake are known as earthquake precursor. Ionosphere changes that recognition by remote measurements (such as using Global Positioning System (GPS)) are known as earthquake ionospheric precursor.TEC (Total Electron Content) of the ionosphere can be achieved by GPS data processing. Classic methods such as mean are unable to detect non linear pattern and therefore in complex and nonlinear systems they are not suitable for recognition and prediction of time series. Because of the nonlinear behavior TEC and land surface changes in order to detect changes, in this paper an attempt is done using an artificial intelligence method including ANN (Artificial Neural Network) and multilayer Perceptron (MLP) for pattern recognition and prediction of TEC variations. Because ionospheric fluctuations usually do not have a normal distribution and do not follow Gaussian curve, in order to detect seismic anomalies, the mean and interquartile range is used to determine the lower and upper bounds. In this study several data sets from the ionospheric total electron content (TEC) derived from the GPS data processing by Bernese softwares. In this way earthquakes of Ahar located in east Azerbaijan (2012/08/11) and Bushehr (2013/4/9) have been studied and the results were compared with data from global stations. First the stations coordinates were calculated using Bernese software with PPP (Precise Point Positioning) method. Then TEC values were obtained using GIM (Global Ionosphere Model). By analyzing the causes of ionospheric anomalies such as the geomagnetic field and solar activity and remove them from the process, results indicate that some of this anomalies caused by the earthquake and using intelligent algorithms could be useful for the prediction of nonlinear time series and outstanding anomalies ocurr some days before and after earthquake. It can be concluded that ANN algorithm has been able to detect TEC anomalies well. Also TEC values are obtained from ground stations have a high correlation with the results of global standard model.

Gravity-field recovery of the Earth using reconstruction of spherical harmonic coefficients up to specified degree and order requires proper data sampling based on Shannon-Nyquist rate. Since, these coefficients are globally significant, the sampling must be done uniformly on the Earth, which it takes much time and expense to collect and process data. Many studies have been done in the field of sampling analysis of spherical harmonics [1, 2]. Sneeuw [2] showed a lack of Nyquist sampling rate can cause aliasing of second type in gravity-field modeling. Recently based on Compressive Sensing (CS) theorem the sampling rate can be substantially reduced and a signal can be approximated in sparse sense with fewer sampled data that has main role in reconstruction. In this case, the desired signal can be reconstructed, using only some base functions, which are most strongly correlated with the problem. Therefore, based on this strategy, the base functions posing the best solution to the problem will be selected and the sampling rate for regional gravity field modeling will be decreased significantly. When we say a signal is m-sparse, it means that there are at most nonzero components in the signal. In this case, only m coefficients of the signal have large magnitude, and others are zero, or have very small values. Here, the desired signal can be reconstructed with its large components without loss of more information. The zero-norm of a vector which is defined as, specifies the sparsity-level of a signal. Sparse approximation has been discussed in many studies [4, 5, 6, 7, 8, 9]. The basic idea proposed by Mallat and Zhang [4] is called matching pursuit (MP), which is an iterative sparse approximation method to reconstruct a signal under specified conditions by replacing a complex sparse problem with a simple optimized solution. Pati et al. [6] modified this algorithm into orthogonal matching pursuit (OMP), which is used for non-orthogonal dictionaries and converges faster than MP. The regularized orthogonal matching pursuit (ROMP) algorithm popularized by Needell and Vershynin [8] is an iterative sparse approximation method where at each iteration m nonzero components of unknown parameters that most closely resemble the properties of the desired signal are selected. Needell and Tropp [9] refined the ROMP algorithm with compressive sampling matching pursuit (CoSaMP), which identifies locations of the large energy of a signal at each iteration. All these algorithms try to find column vectors in the design matrix that most strongly correlate with the desired signal. It is also assumed that the design matrix is well-posed and prior knowledge of the sparsity-level of the signal is clear. Usually, in practical application an ill-posed problem may be encountered, also the sparsity-level of the signal is not exactly clear, which make it difficult to use conventional iterative methods of CS. In this paper we present a new dynamic algorithm called Stabilized Orthogonal Matching Pursuit (SOMP) for gravity-field recovery of the earth using sparse approximation of geopotential spherical harmonic coefficients, which is compatible with the ill-posed problem and can determine the sparsity-level of the signal, properly. Numerical result of the calculated spherical harmonics coefficients up to degree and order 36 shows that the algorithm is able to reconstruct the Earth's gravity-field with precision in mind the number of samples is 50% lower than the Nyquist rate.

Gravity Recovery and Climate Experiment (GRACE) satellite mission has provided a powerful tool to evaluate groundwater resources. In many cases, groundwater resources are nonrenewable, and monitoring the rates at which they are utilized is important for planning purposes. In this study, we have used GRACE level 2 Release 05 data to evaluate groundwater resources across southern Iran (south of 34o latitude) during August 2002 to December 2010. We estimate monthly changes in total water storage (groundwater plus soil moisture plus surface water and snow) across this region using data of GRACE level 2 Release 05, from the Center for Space Research (CSR) at the University of Texas (data available at http: //podaac.jpl.nasa.gov). We replace the GRACE results for the degree-one spherical harmonic coefficient, which correspond to geocentre motion due to the Earth’s mass redistribution, with those computed as described by Swenson et al. [2008]. We also replace it for the lowest-degree zonal harmonic coefficient, C20, which is due to the flattening of the Earth, with those obtained from Satellite Laser Ranging (SLR). The effects of Glacial Isostatic Adjustment (GIA) are small in this region, but are nevertheless corrected by a GIA correction model. Stripping effects in the GRACE data, due to the nature of the measurement technique in GRACE and mission geometry, are smoothed by applying a Gaussian smoothing function with a 350 km radius. The results show a large negative trend in total water storage centered over western and southern Iran. GRACE data have no vertical resolution, in the sense that it is impossible to use the GRACE data alone to determine how much of the mass variability comes from surface water or snow, how much comes from water stored in the soil, and how much comes from water in the subsoil layers (i.e., from groundwater). Because our goal is to isolate the changes in groundwater storage, it is necessary to first remove estimates of the other water storage components. Using output from a land surface model such as a version of Community Land Model (CLM4.5) to remove contributions from soil moisture, snow, canopy storage, and river storage, we conclude that most of the long-term water loss in the southern Iran is due to a decline in groundwater storage. Our estimates show that the groundwater loss during this period is at an average rate of 45 km3/yr. We compare our GRACE estimates over southern Iran with Iranian groundwater estimates obtained from 330 active observation wells, used to monitor the level and quality of groundwater across this region. The results show that the conclusion of significant Iranian groundwater loss is further supported by the in situ well data. These estimates represent the combined effects of natural climate variability (e.g., drought) and human activities. Because CLM4.5 also includes unconfined aquifer storage, we can estimate anthropogenic groundwater trends by subtracting the CLM4.5 predictions of naturally occurring groundwater change from our total groundwater change estimates. These results indicate that 2.99±1 km3/yr of the groundwater loss in southern Iran may be attributed to human withdrawals.

One of the most fundamental and important a new area of research in geodesy is earth surface deformation modeling at local and global scales. Also, check out the effective factors in deformation, and offers various computation methods in order to determine the movement of the Earth's crust are considered as a recent development in geodesy. In recent years, space geodetic techniques with high precision and reliability have provided new sources of information to determine the geodetic positions. This information used for the detection and quantification of surface deformations. Iran is located in a very active seismic region. Cataloge of historical earthquakes in this region shows that Iranian plateau has potential for great earthquakes in the future. Due to this high risk of natural hazard, many researchers have focused to study about geodynamic of Iran. For this purpose, in this paper a new numerical measure called Novozhilov measure of mean rotation is introduced. In the fourth decade of the 20th century Novozhilov obtained a measure of the mean rotation by modifying a previous definition produced by Cauchy. The measure introduced by Novozhilov for the mean rotation indicates the importance of the infinitesimal rotation tensors. To achieve this goal, first linear strain and rotation tensors on earth surface based on shell theory in continuum mechanics using finite element approach will be calculated and then the mean rotation measure using linear strain and rotation tensor components is determined. In this paper the results of Novozhilov’s mean rotation measure were compared with GPS block rotation rates in deg/Myr measured in the center of each block (from block model with locked faults). GPS network that is used in this paper includes 37 stations all over Iran. The computed linear strain and rotation tensors based on geodetic observations (GPS) of national permanent geodynamic network in 2008 are in a good agreement with the numerical results of previous works. The pattern of Novozhilov’s mean rotation measure over Iran shows that the highest right turn rotation is related to the region in the south of Iran including JASC, BABS, GLMT (3.113deg/Myr) stations. Also the highest left turn rotation can be seen in the north of Iran including MAVT, BIAJ, GRGN (-2.509deg/Myr) stations. The importance of Novozhilov’s mean rotation analysis on earth surface in comparison to the analysis of this measure in Cartesian system is shown by this fact that the computed measure on the earth surface is in a good agreement with the results of previous studies on blocks rotation in different areas of Iran.

Monitor and detection of displacement field due to changes in land surface are one of the practical and important studies in different topics of the Geodesy, Geological and geophysical which have a significant role in trends and preventing natural disasters such as earthquake, subsidence and landslide. In the meantime, there are different methods for detection this displacement and geodetic measurement, that among them, Interferometric Synthetic Aperture Radar (InSAR) with a feature wide spatial coverage, fine spatial and time resolution and high accuracy has become one of the important and significant techniques. Land subsidence due to groundwater extraction has been a common geohazards in many arid countries and districts of the world. In Iran, this is a serious challenge for many regions, particularly in the plains with arid and semi-arid climate, for example, Mashhad valley, Hashtgerd, Yazd and Golpayegan plains. Gazvin plain is one of these regions where the land subsidence has seriously developed. This region is located in the north-central Iran, with an area size of about 4430 km2. Gazvin plain in terms of industrial, agriculture and population point is one of the important plain in Iran. In recent years, high rate of extracting water from underground source due to agricultural activity, industrial and population development caused decreasing the groundwater level, so because of the groundwater level downfall, subsidence occurs and it’s trace is seen as cracks and fractures in the ground surface.Studies show that a large area in Qazvin plain is subject to the land subsidence induced by overexploitation of groundwater for the purposes of agricultural, industrial and population development. Therefore, this research study on the pattern and rate of subsidence occurred in Qazvin plain between 2003 and 2010, using radar is Interferometric synthetic aperture radar (InSAR) techniques. The data set consists of 20 and 18 images of descending tracks D192 and D421 during 2003 to 2010. We processed radar data with the open source software StaMPS/MTI (Stanford Method of PS/Multi-Temporal InSAR). The interferograms are corrected for the phase signature due to orbital separation using precise doris orbital data for ENVISAT satellite which is provided by DEOS are stored in a binary format (ODR) that believed to have a radial precision of 5-6 cm. then the 90 m SRTM DEM has been used to estimate the topographic phase contribution. The atmospheric phase delay in each individual interferograms corrected using the retrieved water vapors from MERIS data. We also improve signal to noise ratio of each differential interferogram using a Goldstein filter. The time series analysis of permanent scatterer (PS) and small baseline subset (SBAS) algorithms are used for deformation time series analysis. The time series results show that a considerable and continuously land subsidence in the study area. Results show a good agreement between the PS and SBAS time series, both of two approaches identify peak amplitude of ~ 30-35 mm/year for the 2003-2010 times period. With comparison width and extent of subsidence of InSAR results and plain wells density map of Qazvin plain determines the subsidence occurred in the area with the density of deep wells to extract groundwater and subsequent subsidence occurred in this area.

Movement of objects is taking place in geographical contexts. Context directly/indirectly influences movement process and causes different reactions to moving objects. Therefore, considering context in movement studies and the development of movement models are of vital importance. In this regard, incorporating context can play a crucial role in similarity measurement of objects movements and their corresponding trajectories. Trajectories of moving point objects, beside their spatial and temporal dimensions, have another aspect which is called contextual dimension. This dimension, however, has been less considered so far and a few researches in trajectory analysis domain have investigated it. To this end, this research develops a method based on Euclidean distance in which individual spatial, temporal, and contextual dimensions as well as their integration can be explored in the process of similarity measurement of trajectory. Beside the simplicity of the method, it is developed in a way for taking into account every small change in each type of dimension (s). To validate the proposed method and survey the role of contextual data in similarity measurement of trajectories, three experiments are performed on commercial airplane dataset. Accordingly, geographical coordinates and altitude of airplane as spatial dimension, travel time as temporal dimension, and airplane speed, wind speed, and wind direction as contextual dimension are utilized in these experiments.The first experiment measures the correspondence of trajectories in different dimensions. Also, it explores the role of dimensions weights individually and collaboratively along the similarity measure process. The results demonstrate that weights severely affect similarity values, while they are totally application dependent. Meanwhile, it can be confirmed that contexts may increase or decrease the values of trajectories similarities. This effect can be seen in the average of relative similarity values of commercial airplanes trajectories in spatial (0.60), spatial-temporal (0.51), and spatial-temporal-contextual (0.46) dimensions. Contexts can enhance and restrict movements as well. To justify this statement, the second experiment is conducted to explore how movement and geographical contexts interact in similarity measure process. To this end, four sample trajectories are compared with respect to different dimensions. For a pair of trajectory, the relative similarity value at spatial dimension is 0.04. By incorporating time dimension, this value increases to 0.30 at spatio-temporal dimension. Given the high similarity of these two trajectories in wind direction, wind speed, and airplane speed (0.85), the ultimate similarity of them becomes 0.48. In contrast, for another pair of trajectory, the spatial and spatio-temporal similarity values are 0.85 and 0.91, respectively. Considering the similarity value of these two trajectories in wind direction, wind speed, and airplane speed (0.37), the final relative similarity becomes 0.73. The third experiment sought for the role of motivation context in similarity measure process. Although such context is very difficult to capture and in many applications will remain inaccessible, we consider the pilots decisions in handling the airplanes during the approaching and landing phases (i.e., continuous descent final approach or dive and drive) as the motivation context in this application. Choosing either of these techniques highly affects the figure of trajectories where quantifying them can be accomplished by measuring the similarity of trajectories at spatial and spatial-temporal dimensions. All in all, the results of the above experiments yield the robustness of the proposed method in similarity measurement of trajectories as well as its sensitivity to slight alterations in dimensions.

GPS time series consists of a linear trend, harmonic signals, probable offsets and also noise which is described as a stochastic part. Because of various applications of GPS time series such as plate tectonics, crustal deformation and earthquake dynamics studies, these time series should be modeled with high accuracy. For this purpose, systematic effects in functional model should be determined with high accuracy. In this paper the effect of earthquakes is also considered in the functional model in addition to mentioned behaviors. Because earthquakes cause crustal deformations, their effects can be observed in the shape of offsets (as coseismic effects) and (or) rate changes (as coseismic or postseismic effects) in the time series. Neglecting these effects lead to biased estimation of noise amplitudes. To discover the effect of earthquakes, a manual solution is used for each station. Effects are detected graphically by comparison of behavior of time series and epoch of occurred earthquakes in the region. The earthquakes which considering their effects, lead to the best fitting of functional model to time series, are selected as effective ones. Because the Alborz range is the most seismically active region in the Northern Iran, 25 permanent GPS stations with the time span between 2005 and 2013 in this area are selected for this study. Analysis of time series indicates similar behavior of time series with the same offset times and common earthquake effects for most stations (also for those which are located in far distances from epicenters). This result means that systematic effects may propagate from one station to the others during the processing and the network adjustment. Furthermore, noise analysis of time series using least squares (co) variance components estimation method, shows that neglecting seismic effects can result in the presence of random walk noise in 88%, 12% and 60% of north, east and up components, respectively. However, considering the seismic effects causes positive estimation of variances of random walk noise in 12%, 12% and 36% of north, east and up components, respectively. Finally, due to similar behavior of time series, a reprocessing of them could be suggested.

Land surface emissivity (LSE) is an important intrinsic property of materials and knowledge of the LSE is essential to derive the land surface temperature (LST) that can be obtained from the emitted radiance measured from space. LSE provides useful information for geological and environmental studies, mineral mapping and is one of the important input parameters for climate, hydrological, ecological and biological models. The emissivity of natural surfaces inherently may vary significantly due to differences in soil structure, soil composition, organic matter, moisture content and differences in vegetation cover characteristics. In other words, LSE changes is depending on the surface (such as texture, topography, soil moisture, angular variations effect) and sensor parameters (such as spatial resolution, SRF, and effective wavelength of thermal bands). Remote sensing technology provides widely the monitoring of this quantity. Several methods exist to estimate LSE from satellite data, which apply the visible and near-infrared (VNIR) or thermal infrared (TIR) spectral regions or both of them. According to the way by which the LSE is determined along with LST, the emissivity estimation methods from optical remote sensing data can be categorized into three distinct types including, stepwise retrieval methods, simultaneous LST and LSE retrieval methods with known atmospheric parameters, and simultaneous LSEs, LST, and atmospheric quantities retrieval methods. Influential researches, in the stepwise retrieval methods, were conducted and mainly NDVI-methods have been used to predict LSE from NDVI values. In particular, NDVI-methods assume that the surface is composed of the soil and vegetation, some problems arise for other kinds of surfaces that are likely classified as bare soil pixels, such as rocks, man-made, and ice/snow. Besides, another main origin of error in these methods is caused by great changes in the emissivity of soil types. Furthermore, the choice of a typical emissivity value for some surface objects such as bare soil is a more critical question, because the variability of emissivity values for soils is more than vegetation and other ones. In this research, a new approach called improved normalized difference vegetation index-based method (INDVI_based) estimating LSE on Landsat-8 (known as Landsat Data Continuity Mission, LDCM) data has been proposed for semi-arid areas. At first, a simulation of channel emissivities and reflective bands of basic classes in bare soil, vegetation and mixed areas is accomplished based on convolving ASTER spectral Library with LDCM spectral response functions. Then, for main three areas are defined to determine separate emissivity estimate model as function of reflective bands from basic spectra associated with the main class. In the proposed method, the cannel LSEs are expressed as functions of atmospherically corrected reflectance from the LDCM visible and near-infrared channels with wavelength ranging from 0.4 to 2.29 μm fo bare soil. The effectiveness of the proposed approach was implemented in LDCM data and obtained LSE were compared and validated with two scenes of LSE standard product of Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER). Results showed that LSE of the improved proposed method, in the band 10 of LDCM in comparison with the first and second LSE product of ASTER, lead to 0.76% and 0.75% errors in term of root mean square error (RMSE) measure, respectively. Moreover, this error for thermal band 11 is1.49 % and 1.06% in first and second examined scenes, respectively. Unlike previous methods, the proposed method not only accurately estimates of LSE as a function from the reflectance of various surface objects, but also it uses the spectral response function of thermal and reflective bands in estimating the LSE. In addition, the proposed method the poor relationship between LSE and only reflectance of the red band in previous methods, strengthen due to the use of all reflective bands in LSE estimation and it is applicable on most sensors.

As the number of published geospatial resources in the web is increasing permanently, providing users with proper tools for search and discovery of these resource is of great importance. Geoportals are a type of web portals that enable users to find geospatial resources. However, when users search for a specific resource in existing geoportals, a wide range of unrelated results might be retrieved. Such results are often confusing, so that user has to spend a lot of time to find the most appropriate resource. In addition, working based on user desires and preferences has been considered as a fundamental characteristic of modern web applications.In this article, the capability of recommendation is added to the geoportal. The recommendation capability enhances the ability of geoportals which is able to recommend the most appropriate geospatial resources to users by considering their desires and preferences. In order to accomplish this purpose, a new solution with recommender systems is proposed. Recommender systems using knowledge derived from the users’ previous interactions can lead users to access resources that they are interested in. Collaborative filtering is a common technique used in recommender systems that deal with this problem. This method uses user rating data to extract the similarity between users or resources for making recommendations. Moreover, mathematical functions are defined in order to improve the efficiency of the recommender geoportal process. The functions are designed according to the specifications of users and resources such as city, county and language, along with the distance between the user and resource.In addition, an operation of these functions is to obviate the cold-start problem in collaborative filtering. To evaluate the designed geoportal recommender, a recommender geoportal is implemented so that a user is able to profit the advantages and usage of its recommendations. The obtained results indicate that the efficiency of recommender geoportals is improved compared with common geoportals.

Therefore, in this article our studies were focused on this problem. In order to correct these errors, an independent source of information was required such as imaging from another angle, topographic map or DEM. In this paper, a method for geometric calibration of SAR images is proposed. The method uses Range-Doppler (RD) equations and to implement the method used in this article, two SAR datasets are tested with RD modelling. These datasets are acquired by ALOS PALSAR spaceborne SAR sensor. Test areas covered by these datasets range from flat plains to mountainous areas, which the first dataset located in the border between United States and Mexico and the second one is in Iran. In this method, for the image georeferencing, the appropriate Digital Elevation Model (DEM) and also exact ephemeris data of the sensor is required. In the algorithm proposed in this paper, first digital elevation model transmit to range and azimuth direction. By applying this process, errors caused by topography such as foreshortening is removed in the transferred DEM. Then, the original image is registered to transfer DEM by transformation equations. The output is a georeferenced image without geometric distortions. The advantage of the method described in this article is in eliminating the requirement for any control point as well as the need for attitude and rotational parameters of the sensor. Furthermore, two experiments with different settings are designed and conducted to comprehensively evaluate the accuracy of the SAR georeferencing with RD model. Few experiments are done in this study for various purposes. The first one is to find the best transformation equation among the three types for registering images. In the first experiment the efficacy of three types of transformation equations on georeferencing of ALOS PALSAR images were evaluated with identified check points. To evaluate the accuracy of the georeferenced images, 25 check points in different parts of the image was selected. By comparing the obtained coordinates in georeferenced image and reference points in Google Earth, the RMSE was calculated for these points. In best situation, the planimetry accuracy were 20.11m for dataset A and 19.94m for dataset B and the altimetry accuracy were 30.28m for dataset A and 30.71m for dataset B. Since the ground resolution of multi-look image was 30 meters, the planimetry accuracy achieved in this research is acceptable. The other experiment is to compare the georeferenced SAR images generated from three DEMs to demonstrate the effectiveness of DEM spatial resolution on the accuracy of georeferencing SAR images. In addition we investigated the suitability of three typical DEM datasets for SAR georeferencing in RD model. The experimental results show that the best transferred DEM was obtained from the ASTER DEM of spatial resolution comparable to that of ALOS PALSAR images.

The Alborz range of northern Iran is a region of active deformation within the broad Arabia-Eurasia collision zone. The range is also an excellent example of coeval strike-slip and compressional deformation, and as such can be an analogue for inactive fold and thrust belts thought to involve a component of oblique shortening. It is roughly 600 km long and 100 km across, running along the southern side of the Caspian Sea. Several summits are 4000 m in altitude. Damavand, a dormant volcano, reaches 5671 m. The highest non-volcanic summit is Alam Kuh, at 4830 m.The Alborz range, northern Iran, deforms by strain partitioning of oblique shortening onto range-parallel left-lateral strike-slip and thrust faults. Deformation is due to the north-south Arabia-Eurasia convergence, and westward motion of the adjacent South Caspian relative to Iran. The occurrence of moderate to large earthquakes in the Alborz suggests an important deformation regime in this mountain belt. This belt has been responsible for several catastrophic earthquakes in the past. The Manjil earthquake of 20 June 1990, which is the most disastrous Iranian earthquake in the twentieth century, occurred in this belt. Both thrust and strike-slip faulting have been reported in this belt.By the knowledge of the crustal deformation characteristics in areas with active tectonics we can realize the style, direction and magnitude of the deformation in the area, it can contribute to the deeper understanding of the underlying tectonic processes and to the improvement of the seismic hazard assessment.In this contribution strain rate fields (using geodetic data vs. seismic data) calculated over the three different parts of Alborz regions (Western, Central, and Eastern Alborz). Eigenspace components of seismic strain tensor (seismic events with Ms³4.0 in the time interval 1900-2010) over three zones revealed crustal shortening over all three zones. Namely, the results of seismic data showed left-lateral strike-slip faulting in the Eastern Alborz, the right-lateral strike-slip motion in the Western Alborz and compression mechanism in the Central zone. The highest compressional rate in the Western Alborz probably represent the high seismicity rate of this region. In comparison, eigenspace components of geodetic strain tensor (during time interval from 2005 to 2009) illustrated high rate of compressional components in the Central and Western zones. Comparison of seismic and geodetic strain rates in the Western Alborz indicates the deformation rate over this region is associated with seismic activities. However, in Central and Western regions the geodetic strain rates appeared remarkably larger than seismic strain rates. May, this fact illustrated the deformation pattern in these regions are related to the local aseismic creep of those segments. The difference between eigendirection of both kind of tensors (geodestic vs. seismic) in segments probably were related to the short period of GPS data with respect to the seismic data. However, it is a fact that early historical data are incomplete. So, the catalog completeness is crucial for estimating reliable seismicity strain rates and, consequently, for use in seismic hazard assessments. Hence, the performing the repeated geodetic data over the Alborz region is proposed to investigate the reliable estimating of the strain rate.

Time series is a type of data with complex structure. Analysis of time series is used in sciences such as meteorology, economics, geology, marine science, medicine and engineering widely. So, Because of time series applications in various sciences, the interest to analyze these data has been increased. On the other hand by developing information gathering technologies such as mobile, GPS and sensors, and Access to large volumes of time series data, we always require methods to extract useful information from large datasets. Thus, data mining is an important method for solving this problem. Clustering analysis as the most commonly used function of data mining, has attracted many researchers in computer science. Clustering is a strong instrument for knowledge discovery and it provides useful information about existing patterns in datasets. In general, the purpose of clustering is representing large datasets by a fewer number of cluster centers. It simplifies large datasets and thus is an important step in the process of knowledge discovery and data mining. Fuzzy C-means (FCM) clustering is one of the most important classic clustering methods that have been used in many researches. The main disadvantage of this method is the high probability of getting trapped in local optima especially in facing high-dimensional data such as time series. Furthermore Euclidean distance is the most commonly used similarity measure in Fuzzy C-means but sometimes, its necessary to use another similarity/dissimilarity measures instead of Euclidean distance. In this paper in order to compensate the shortcomings of Fuzzy C-means algorithm, we used one of the existing evolutionary algorithms. Evolutionary algorithms has gained huge popularity in the field of pattern recognition and clustering recently. Among the existing evolutionary algorithms, the differential evolution algorithm as a strong, fast and efficient global search method has been attracted the attention of researchers. In this paper, we proposed a technique for clustering time series data using a combination of Fuzzy C-means and differential evolution (DE) approach and we considered dynamic time warping (DTW) as distance measures between time series. Also, in this method we used Discrete Cosine Transform (DCT) to time series dimension reduction. Finding all elements of cluster centers using differential evolution is time consuming and the large number of unknown parameters related to the cluster centers will reduce the efficiency and the speed of differential evolution algorithm. So, for reducing the search space, the most important Discrete Cosine Transform coefficients of the cluster centers were recognized as the main unknown clustering problem in the proposed method and differential evolution algorithm tries to determine the near optimal Discrete Cosine Transform coefficients of cluster centers by minimizing the Fuzzy C-means objective function. Experimental results over two popular data sets indicate the superiority of the proposed technique compared to fuzzy C-means and a clustering algorithm based on differential evolution without using dimension reduction techniques. Comparing the run time of the methods, the proposed method is slower than the Fuzzy C-means clustering algorithm, but due to the use of discrete cosine transform method to reduce unknowns, it operates faster than differential evolution without using dimension reduction techniques.

The rapid growth and development of urban environments has been created a lot of motivation for researchers in Geomatics engineering in order to provide optimal methods for monitoring urban changes and updating maps. Nowadays, using aerial/satellite imagery for updating old maps is one of the important topics in Photogrammetry and Remote Sensing. In this case, the available information in the digital cartographic data can be used as training data for classification, creating conceptual model, reducing the search space and also to estimate the unknown parameters like segmentation parameters. Applying available information of cartographic data leads to contribution this type of information during the feature extraction in order to improve the efficiency and decrease the defects of this progress. Therefore, this paper proposes a novel approach for building extraction in order to building map updating from aerial images with help of old digital cartographic data. In this study, the geometric information of polygons existing in old cartographic data is used as an auxiliary data to improve the process of building extraction and change detection based on active contour models in a hierarchical approach. The building extraction process is done in two step using two types of active contour models which runs upon the height and spectral data. The active contour models in the face with large dimensions, high level of details and also images with weak gradient information have not acceptable performance. Therefore, the focus of this paper is to present a novel approach to compensate the above mentioned defect. So, the building extraction process is done in a hierarchical approach based on a combination of two geometric active contour models which causes the elimination of the defects of these models in the extraction of buildings with different geometric and spectral behaviors. In the proposed method, each of the polygons are considered as an initial curve to a region-based geometric active contour model. This model is run upon a part of the DSM, commensurate with the position and dimension of the old polygon. After primary extraction of the building boundaries from the DSM, geometric change detection is done and then, the change map is produced. The change map gives a comprehensive intuition about the occurred changes. Due to various errors in DSM, the primary extracted boundaries did not have sufficient accuracy. So, to improve the accuracy of these boundaries, the results are introduced to a constrained edge-based geometric active contour model which is one of the innovations of this study. The common edge-based active contour models cannot recognize the object boundary in images with weak gradient information and so, the level set function evolution well be unstable and therefore will not be getting correct results. To solve this problem, a novel approach as constrained level set formulation is proposed. This constraint is derived from output of the region-based model and is caused to solve the deficiency of this model in the face with weak gradient images. After the extraction of the precise boundaries, the MBR-based approach as an approximation or generalization technique is applied for irregular generated boundaries of changed buildings which are obtained from the proposed constrained edge-based model. Finally, the generalized polygons are record in geodatabase. The dataset used in this study concern the part of vaihingen city of Germany. The shape accuracy of extracted buildings and overall accuracy of change detection process were 92% and 78%, respectively. These results clearly demonstrated the success of the proposed method in building extraction using active contour models and change detection in order to automatic building map updating.

Changes in socio-economic trends and factors that affect on the health of a society as well as changes of environmental that occur over time, can alter the effects of a disease at different times. At the level of city, Identify regions with high risk of getting a particular disease that can be lead to death, Compared to other regions and investigate the behavior of any of these diseases during different time periods, an important role in disease control and health management in the community. In this study, investigated the trends of disease-based mortality in different regions of Tehran with revolving around seasonal and annual changes during the period 1993-2013. Geostatistical analysis using different tools to assess the relationship between disease at a certain time and in one place with the surrounding area. The Local Moran's I index is Including neighborhood analysis tools that to assess disease in specific time and place are effective to neighboring regions and find the neighborhood at different times, to identified regions of high risk (HOT SPOT). Moran index value articulated the relationship of a variable and its distribution in space and time with neighboring regions. diseases of Hot Spots and classification them in spatial clusters on the basis of this index, can be identified. Neighborhood is defined in such a way that at the level of a city is different for various diseases. This optimum amount neighborhood may be affected at different time intervals depending on the nature of the disease and also affected it is different from local factors or global. Results of the analysis on 12 group’s mortality disease-based showed that spatial significant levels are different in each disease. The relationship between diseases in different regions may be clustered in regions or dispersion they exist. This amounts to 5 groups of diseases, including diseases of the brain, liver, gastrointestinal and bleeding, cancer and stomach cancer have a meaningful relationship with the spatial factor’s in Scaling throughout a city. It is worth mentioning that This relationship may be different in larger scale than the area of the city. In the next stage, using temporal of analysis for five groups of diseases that have higher spatial index, Amount dependence of any of the diseases during the time periods of annual and also examined changes of any disease in different season. The results showed is different neighborhood radius for different diseases in different seasons. Also regions with a high risk of each disease being different with the change of seasons and changing environmental parameters. In examined the results of annual trends showed that during twenty year period, Amount dependence of any of the diseases to locations due to changes In socio-economic status and health of the community is quite different.

Land use suitability assessment is a traditional problem and many researches have been undertaken to address this problem. The main reason why this problem is important is that experts want to consider all the aspects into account when they are trying to find the optimum location for a specific purpose. In other words, experts want to find the best place from all point of views such as environmental, ecological, economic and political aspects. Therefore, a decision support system is obligatory in order to facilitate decision making using mathematical models. On the other hand, due to the fact that a place is going to be chosen in this problem, GIS is a main science involved in this assessment. In this paper, a spatial decision support system is proposed using the integration of Sugeno integral and Imperialist Competitive Algorithm (ICA). Sugeno integral is able to aggregate alternative scores with respect to their interaction. In other decision making methods, it is assumed that the criteria are independent but it is against the real world situations. For example, in land use suitability assessment problem, some criteria such as land price and distance to major roads are not independent. Therefore, this study can improve spatial decision support systems by taking the impact of interaction among criteria into account. Sugeno integral operator uses fuzzy capacities instead of layer weights. Fuzzy capacities show the importance of each group of criteria for land suitability assessment. Furthermore, Sugeno integral can provide a number of numerical measures to indicate the importance of each criteria (Shapley value), the interaction among each set of criteria (interaction index) and the power of each criteria to veto the final decision (veto index). Shapley value is a parameter defined by game theory which indicates the power of each player in a game. In terms of decision making problem, Shapley index shows the importance of each criterion in the decision making process. A more important criterion has a higher impact on the results of the decision making. Interaction index shows how two players cooperate. If the two players have a positive cooperation they will make a better situation and if they have negative interaction the power of their coalition will be less than the power of each of them. In multiple criteria decision making, interaction index represents how two criteria interact. When the simultaneous satisfaction of two criteria is favourable, the interaction among them is positive and when the simultaneous satisfaction of the two criteria is not what the decision maker wants, it means that the two criteria have negative interaction. In this research imperialist competitive algorithm is applied to find the best values of fuzzy capacities that best describe the experts’ knowledge. In other words, a constrained optimization problem is solved here to compute the optimum value of fuzzy capacity for each set of criteria. ICA is selected because it is able to find the optimum value of a continuous function under constrains. The proposed SDSS is employed for land use suitability assessment for a new power plant. The results indicate that the method is highly suitable for modeling GIS-based decision making over interacting criteria. This model may be used in other areas of decision support systems with minor modifications.

Nowadays, considering the importance of marine commerce, monitoring the marine navigation and routing the ships could be regarded as important issues. Moreover, specifying the weather condition of the marine environment for minimizing the damages and fatalities to vessels, crews and cargos is vital. Hence, weather routing is absolutely crucial. In addition, due to the high cost of voyage, the duration of voyage is one of the essential parameters of weather routing.The aim of this research is to minimize the voyage duration regarding the weather conditions. The marine environment is simulated by a grid of weather data which has the resolution of 0.25 D that is updated every 6 hours. The data is downloaded from European Centre for Medium Range Weather Forecasts (ECMWF). Following that, the weight of each edge is calculated with respect to the time in which the vessel passes the edge. Travel time is related to the impact of wave, wind and sea depth on vessel’s speed which is computed based on Kwon method and Lackenby’s formula. Finally Dijkestra algorithm is applied for calculating the optimum route.The studied area located in the north of Indian ocean in Persian gulf, Oman Sea and Arabian Sea. The model is implemented in two different weather conditions (calm and rough conditions) for calculating minimum time route between Pipavav port in India and Bushehr port. The results indicate that although the voyage distance increased in this model, the duration of voyage decreased. Thus, the cost of voyage dropped noticeably. In addition, the depth of the marine environment determines the route of journey in the calm weather conditions because of lack of existence of high seas and storm in front of the ship. In the rough weather conditions the weather condition parameter (speed and direction of the wind and height and direction of the wave) has more effect than depth parameter in order to prevent the ship from navigation in high seas in which the ship's speed reduces dramatically. Moreover, results show that in the bounded seas like Persian Gulf with small area with respect to spatial resolution of marine environment (the resolution of the weather data) in which the weather condition between neighbouring cells does not change obviously, depth parameter is the critical parameter to determine the journeys path.

Many countries aim to design and build Spatial Data Infrastructure (SDI) to facilitate, manage and share spatial data. Different public or private organizations provide data sources in diverse ways and various contextual situations such as weather conditions, coordinate system definitions or acquisition times. Therefore, SDI should be semantic-based, as possible as it can, to deal with different user languages, requirements. Such an SDI can help providing appropriate representation and search. Since the data integration is an essential part of each information system, semantic similarity is getting more attention in the web world. An efficient spatial data sharing across different organizations is considered to have significant contributions to the sustainable development of today’s communities. As the quantity and accessibility of spatial data is tremendously increasing via web, interpreting, handling and retrieving of this data has become a difficult task. The data suppliers come from various information communities with differing conceptualizations of the world. So, this data is heterogeneous in essence and distributed over several sources. Since the acquisition of geospatial data is extremely expensive, developing mechanisms for reusing and sharing geographic information are necessary to save costs. Besides, customer orientation and personalization of data sources is central to enable flexible and multipurpose usage of the data and to provide customers with the required data. Ordinary information retrieval systems are limited to syntactic retrieval mechanisms and therefore cannot deal with semantic differences in the customer's and the data supplier's conceptualization. The Open Geospatial Consortium (OGC) has established standards for storing, discovering, and processing geographical information but these standards cannot solve the semantic problem. Today, the semantic heterogeneity is considered as the main obstacle to the full interoperability among spatial data sources. Geospatial data describes real world geographic features by their spatial extent and their location. Hence, properties are necessary to capture the semantics underlying geospatial data, because they can represent spatial qualities such as shape. The notion of semantic similarity serves as an indicator for relevance in the retrieval process.This paper uses an ontology-based approach and description logic to resolve the semantic heterogeneity. For this purpose, semantic similarity measurement is used to interpret, handle, and retrieve data in terms of semantically similar concepts. In order to calculate similarities, two existing similarity measurement models were combined: Feature model and Network model. While Feature model computes similarity of concepts based on their common and distinctive properties, Network model puts the concepts in a semantic network and computes the similarity based on the relations of the concepts in the network. This paper proposes a hybrid similarity model as a computational model for semantic similarity measurement. This hybrid model enables the necessary expressiveness to capture semantics underlying geospatial data. The shortcomings and benefits of each model with respect to the requirements of semantic information retrieval of geospatial data are described. Retrieval systems use similarity measures to determine the relevance. Only a retrieval system which returns cognitively adequate results can successfully support human users. The proposed model retrieves relevant information by measuring the semantic similarity of concepts to a given query. The methodology has been tested on some parts of Iranian Water and Wastewater Company’s infrastructure as a case study. Since semantic similarity is an appropriate means to resolve semantic heterogeneity in retrieving data in SDIs, the proposed model can help users by representing similarity in a quantitatively manner. This paper has considered blockage in pipeline as user search concept. The results of similarity represent the advantages of the proposed model. In addition, the results showed that the most similar concept to user search concept was Elbow with %42.5 similarity because of its curvature.