JOURNAL OF GEOMATICS SCIENCE AND TECHNOLOGY
Year:2016 | Volume:6 | Issue:2|Papers Count:10

The geospatial data quality validation is building an effort to develop quality standards for geospatial information. The growth of Volunteer Geospatial Information (VGI) raises many issues regarding quality control or quality assurance. The lack of metadata and standards combined with an unknown motivation and used credibility lead to heterogeneous datasets with unknown quality. Thus, two main approaches have been suggested for assessing the quality of VGI: first approach is based on comparing VGI with authorized data and second approach tries to validate VGI without using authorized data.The methods based on first approach need the authorized information and this information is not always accessible or in case of existence might be expensive. Moreover, the method with global efficiency is not reachable. The methods based on second approach generally are three types: reviewing and correction by volunteers, using the general rules of geospatial data and extracting information from VGI to validate itself. The methods for reviewing and correction by volunteers argue that crowdsourcing data converges on the truth if people have the opportunity to review and correct errors. But features that many people have an interest will be more accurate than features that are of interest only to a few. In second methods, general rules for geospatial data like logical consistency used for VGI validation. The last method tries to use information from VGI to validate itself. For example a method determines information trust just by using the data history like production, change or removing of data to validate VGI just with itself. In this article we try to present a method for validation of quality of VGI without using authorized data and by using of other VGI. In this article, calculable characteristics of OpenStreetMap (OSM) data, as one of the important sources of VGI, are used for validate the quality of its VGI data. Specially, we analysis the accuracy of classification of OSM roads network as part of semantic data of VGI. Roads, such as motorway and residential, have different design and characteristics according to their function in street network. It is tried to classify these roads by finding and learning these distinguish characteristics of each road class. Machine learning models with decision tree and neural network algorithms are used to learn roads characteristics from OSM street network data. Decision tree and artificial neural network with multilayer perceptron are usable for data that contains errors and we have to assume VGI data always have errors. These two Statistical analysis, precision and recall are used for assessing final models. In accordance with the result, Decision tree model have 84.1% weighted average accuracy and represent a suitable model for this method. These methods are based on extractable information from VGI and could be used for any street network to classify the streets.

Gravity gradient tensor is a matrix containing the second order derivatives of the Earth’s gravity field, which has numerous applications in geodesy and geophysics. To date, much effort has been done for estimating gravity gradient tensor with reasonable accuracy. This quantity can be estimated via using various methods, and one of these methods is applying finite-difference method to gravity observations. Finite-difference method can estimate gravity gradient tensor directly by using the mathematical concept of gradient, regardless of extra assumptions. This ability of finite-difference method, from theoretical point of view, provides the possibility of accurate estimation of gravity gradient tensor without considering additional assumptions to the problem. This study tends to introduce and evaluate Finite difference method for estimating the gradient tensor and present formulae for determining gravity gradient tensor from land-based gravity observations. In this paper, the proposed equations are numerically tested by means of using a global gravity model of the earth. Global gravity model of the earth (EGM 2008) is a geopotential model of the earth consisting of spherical harmonic coefficients up to degree 2190 and order 2159. There are numerous uses for these high degree potential coefficient models. One of these uses is modeling and estimating gravity gradient tensor.Finally, gravity gradient tensor is estimated by the proposed method for 6350 gravity stations located in Costal Fars region in a northern part of the Persian Gulf, between the latitudes from 26.5 N to 27.27 N and longitudes from 53.41 E to 55.58 E. The target area is about 10000 square kilometers. About 8500 square kilometers of the study area is located in moderate mountainous regions, and about 1500 square kilometers is located in flat coastal areas. The altitudinal distribution and spatial distribution of gravity in study area are shown in figure 1 and 2 respectively. Numerical experiments of this study demonstrate the ability of this method in gravity gradient tensor estimation with acceptable accuracy. For example, numerical experiments showed that the proposed method can estimate diagonal components of gravity gradient tensor (second order derivatives of the Earth’s gravity field in east, north and vertical directions) with the accuracy values of 12.46, 34.49 and 454.82 Eotvos respectively. The spatial distribution of the gravity gradient tensor components obtained from finite difference method in study area are shown in figure 3.Finally, according to the theoretical concepts discussed in this paper, It can be said because the finite difference method using from derivative and difference concepts directly for estimating gravity gradient tensor, it is expected that this method provide accurate estimation of gravity gradient tensor, As this is happen in the simulation conducted. However the accuracy of this method is very dependent on distances between sampling stations and by reducing distance between the stations, the accuracy of proposed method will be increased. The numerical results of this study also showed that the proposed method can provide accurately estimate of gravity gradient tensor components In some stations surrounded by suitable spatial distribution of gravitational observations.

Photogrammetry and Remote sensing researchers pay attention to urban phenomenon detection; such as building and tree from aerial and satellite image with LiDAR data in the recent decades. Different classification and detection methods in order to use in urban area faced with complex problems, such as small trees and buildings, unfavorable boundary crown, roof covered with vegetation, buildings that are surrounded by trees and vegetation located in the shadow. In this research to improve the problem mentioned above, in the first step the following works has been carried out, preprocessing LiDAR data That in the preprocessing step, noise and outliers of the LiDAR data detected by using Frist and Last Range images and then to calculate the proper height for them used the weighted averaging method than the distance, then at this step generation digital terrain model and normalized digital surface model that for generating digital terrain model the method local base filtering is used that has two stage 1- select training data 2- applying filter with using training data that all part of the first step is implemented in the MatLab 7.12 . In the second step geometrical, spectral, textural and combined features have been produced according to the mentioned problems that to produce these features used from normalized digital surface model and first range of LiDAR data. Then these features by using the SVM_GA algorithm for detecting suitable they are used.SVM_GA algorithm implementation is done in tow rounds, that in first round same features are evaluated with each other and in the second round selected features in first round are evaluated with each other. Finally after three times running SVM_GA algorithm 27 features are selected. From the production features vegetation index combined with shadow area and geometrical features produced from LiDAR data have crucial role for trees detection. In the third step support vector machine classification has been used in order to trees and building detection in the object-based and pixel-based levels with using suitable and selected features. Training data for pixel level classification with using SVM classifier are selected semi-automatically in four classes include road, tree, building and vegetation. For this purpose, the following features are used nDSM, Laplacian and Combined_Index NDVI.for object-based classification with SVM classifier we use training data that they were selected manually and for segmentation of image we use multi resolution segmentation in eCognition 8 software by using the flowing layers, green, red, NIR and Frist range that segmentation parameters are set manually. In order to improving the results of each levels of classification in fourth step we improved with post-processing methods that in this step for both level of classification we have used erosion and dilation morphological filter with different size, so that in object-based level classification, segments of buildings divided into two group: high and low buildings so that for each group of buildings employed morphological filter with different size Separately and then for results of object-based classification we have used a conceptual process for improving object-based level classification. Due to high spectral similarity between two group buildings-roads and vegetation-trees and miss-classification, accuracy, correctness and quality of results have reduced. To improving results have been used neighborly relations and conceptual thresholds in five steps. In fifth step due to the power of each level classification, we try to improve buildings and trees detection with use to fusion object-based and pixel-based classification results, that in this step for building and tree classes have been used different fusion algorithms. The main basis of fusion for building’s class voting results of two level classification and for trees class height pixels in segments and again voting. Results show that fusion of pixel-based and object-based classification improve buildings and trees detection accuracy especially in small and low objects of trees and building also improve crown detection.Fusion the classification results for trees class has more improvement compared to buildings class. Object-based classification level due to using the segments and similarity between trees and vegetation leads to pixels of vegetation not be detected, that in the segments of trees and with use to fusion method for both level of classification, can be detected low and little trees. Results of this method with regards to improving detection in boundary of trees and buildings have positive effect for object extraction. Also suggested method can be detected building with low height and area. After post-processing for detected trees and building classes specified pixel level can be show more capable than object level in detect small object. Finally, results of detection and each levels classification evaluated with reference data, That buildings and trees detection in object level have correctness 0.961 and 0.65 and they have overall accuracy 0.97 and 0.943 respectively, and in pixel-based level they have correctness equal to 0.953 and 0.632 and overall accuracy 0.961 and 0.94 respectively. After the fusion they have correctness 0.971 and 0.718 and they have overall accuracy 0.975 and 0.957 respectively.

This paper aims at combining the geodetic gravimetric inverse problem with the geophysical one to do ‘local gravity field modeling’ and ‘topographic mass-density anomaly determination’ simultaneously. For this purpose, it recalls the basic theories of the two types of gravimetric inverse problems and finds certain relations between their corresponding unknown parameters. The current research proposes a method, which is based on certain anharmonic Radial Basis Functions (RBFs) as well as Generalized Tikhonov regularization method, for modeling the gravity field at the Earth’s surface.Then, it shows that the proposed method can provide certain formulae, based on a combination of the Newton’s IE and the Poisson’s PDE, for extracting the mass-density anomalies from land-based gravity data.Finally, the proposed method is numerically examined by a case study at which the proposed method has been applied to 6350 gravity stations scattered over the coastal Fars of Iran in the north of the Persian Gulf. The case study numerically indicates the possibility of the main idea of the research for simultaneously solving the two types of gravimetry inverse problem.1.Gravimetric inverse problems: From point of view of mathematics, a geophysical gravimetric inverse problem can be defined by the following equation: where the mass-density anomaly is considered as the unknown and defined by subtracting the mass-density functional from its prior knowledge; is the Newton integral operator; is the gravity anomaly yielded by applying the gravity reduction to land-based gravity observations. In a local coordinate system which z-axis is a tangent to the plumb line and outwards from the Earth’s surface, the above geophysical gravimetric inverse problem (i.e. Eq.) can be re-written as follows: The above equation is a type of the Newton’s IE at which is the position vector, is the topographic space of the region of the interest, and is the differential volume element at the position .On the other hand, the above definition is so similar to the definition of the gravimetric inverse problem associated with the Earth’s gravity field modeling in physical Geodesy. The Earth’s gravity field modeling in physical Geodesy is often involved with various types of gravimetric inverse problem such as downward continuation, at which the incremental gravity observations such as gravity anomaly are also placed at the right side of corresponding equations and the unknowns are also placed under the mathematical operators. Land-based, airborne, ship borne and satellite gravity data, as well as astronomical, leveling and tidal observations are counted as common observations of physical Geodesy, which are usually used for modeling the Earth’s gravity field. In this way, the unknowns are usually constituted by coefficients of certain algebraic expansions which enable us to model the Earth’s gravity potential as well as its derivatives such as the gravity vector and gravity gradient tensor. These algebraic expansions are commonly linear and obtained by discretizing linear integral equations achieved by Geodetic Boundary Value Problems (GBVP). Furthermore, these expansions can also be obtained by certain basis functions such as spherical harmonic functions, radial basis functions (RBFs), Slepian and wavelet basis functions. Hence, the current research considers the following equation as the general form of the gravimetric inverse problem associated with the subject of the gravity field modeling in physical Geodesy: where is an operator which depends on either the corresponding integral equations or the expansions of the basis functions; and the vector comprises of the unknowns which model the Earth’s gravity potential and its derivatives. Now, if the Earth’ gravity potential is able to be expanded by a set of basis functions such as, the above general form of the gravimetric inverse problem (i.e. Eq.) will have the following form in the above-mentioned local coordinate system. where and. This paper comprises of an idea of correlating the concepts of the abovementioned gravimetric inverse problems with each other. Hence, first it analyzes each one of the abovementioned gravimetric inverse problems separately and then combines them with each other by finding mathematical relations between their unknowns. For this purpose, the concept of the Poisson’s PDE is used for converting the unknowns of Eq. (i.e.) into the unknown of the Newton’s IE (i.e. the mass-density anomaly). Indeed, the theories discussed by the paper will also examined by certain numerical experiments in a real case study based on 6350 gravity stations scattered over a part of the north coasts of the Persian Gulf.

Change detection using remotely sensed data has been used in many applications, such as the detection of dynamic changes in land cover and land use, the monitoring of forestland and agricultural land, the assessment of damage from natural disasters, and the study of urban environments. Despite the numerous studies devoted to multispectral and hyperspectral imagery, however, the use of optical imaging sensors is limited to weather conditions. Synthetic aperture radar (SAR) sensors can obtain daylight, cloud coverage, and weather-independent images. Their backscattered signals are also sensitive to the form, orientation, homogeneity, and surface conditions of a target. SAR imagery can therefore serve as a useful tool for detecting land cover and land use changes. The development of SAR techniques has given rise to Polarimetric SAR (PolSAR) systems, which measure four linear polarization channels (i.e., HH, HV, VH, VV) and the phase differences among them. SAR polarimetry with the functionality of identifying different scattering mechanisms can provide more significant information than single-channel imagery. From the perspective of image analysis unit, Change detection techniques are classified into two categories, namely, pixel-and object-based approaches. Pixel-based methods rely only on the information derived from individual pixels and do not consider the spatial relationships among these pixels. Whereas, the values of neighboring pixels in an image are strongly correlated, and the probability of change occurrence in adjacent regions is more than distinct points. The use of spatial features also effectively reduces the speckle effect in PolSAR images. Given these considerations, object-based approach has been widely used for PolSAR image analysis over recent years. This paper proposes a novel image segmentation algorithm for improving the accuracy of land cover change detection. This method consists of the following three steps: 1) segmenting two PolSAR images by new segmentation technique, namely region based improved watershed; 2) selecting the optimal differential of polarimetric features based on the Genetic Algorithm (GA) and Jefferies-Matusita (JM) distance criteria; and 3) The binary classification of image objects using the differential of mean pixel values of the corresponding image objects. Despite the development of various region-based segmentation methods, watershed segmentation is appropriate for the segmentation of high resolution images based on the many advantages of this morphological algorithm. These advantages include inherent simplicity, high speed implementation, the creation of separated regions in low contrast images, and the provision of closed connected regions. Common watershed segmentation approaches, such as distance transform and the gradient method, cause over-segmentation problem given the noise or local irregularities present in a gradient image. Unlike the direct application of the watershed algorithm, using a marker-controlled approach that involves the incorporation of additional knowledge into a segmentation procedure can limit the number of segmented regions. In this method, the flooding procedure begins from a previously defined set of markers. Markers, as connected components belonging to specific areas of an image, can be defined on the basis of a set of descriptors, such as gray level value, shape, location and texture. Compared with conventional watershed and multi-resolution segmentation methods, the improved watershed reduces the speckle effect in PolSAR images and avoids the over- segmentation problem. The results of proposed change detection method on Uninhabited Aerial Vehicle Synthetic Aperture (UAVSAR) full polarimetric images achieve the overall accuracy of 92.40% and the 0.85 kappa coefficient.

Detection of pathogenic factors, identify the spatial accumulation of disease cases and finding its distribution pattern are of urgent need in the field of public health and disease management and control. Leptospirosis is a zoonotic disease which occurs worldwide but is most common in tropical and subtropical areas with high rainfall. Wet and mild weather conditions in the northern provinces of Iran have put these areas at high risk for Leptospirosis incidence. The main objectives of this study are to investigate the annual pattern of Leptospirosis distribution, identify the spatial and spatio-temporal clusters of the disease and generate the annual predictive map of spatio-temporal distribution of Leptospirosis at the district level in the Northern provinces of Iran. In this study, Leptospirosis incidences, census data and topographical and climate factors have been used to generate the annual predictive map of spatio-temporal distribution of Leptospirosis. The Leptospirosis incidences are continuously recorded by the Center for Disease Control and Prevention of Ministry of Health of Iran. The population census count estimates for period 2009-2014 were obtained from the Statistical Center of Iran. Topographical data were used to generate the altitude, slope and aspect maps. Climate data such as average temperature, average humidity, annual rainfall and number of freezing days were used to model other affecting parameters. Global clustering methods including Moran’s I and general G indices were applied to investigate the existence of spatial autocorrelation between cases of Leptospirosis and also analyze the annual spatial distribution of the existing patterns. Results of both Moran’s I and general G indices indicated meaningful persistent spatial autocorrelation between Leptospirosis cases and highly clustered distribution of Leptospirosis. Additionally, presence of spatial clusters of Leptospirosis and detection of high risk areas of disease were investigated using the local Moran’s I and local G indices. The results of the local Moran’s I and local G indices identified significant spatial clusters of Leptospirosis cases located in central, north-eastern and western parts of Guilan, Mazandaran and Golestan provinces, respectively.Geographically weighted regression (GWR) and multilayer perceptron neural network (MLP) models were used to generate the annual predictive map of spatio-temporal distribution of Leptospirosis and modelling the relation between the distribution of Leptospirosis cases with topographical and climate factors. Performance of GWR and MLP models were compared using Kappa coefficient, RMSE, MAPE and R2 measures. The evaluation results showed that the MLP model was able to predict the incidence rate of Leptospirosis in 2014 for each district with acceptable accuracy. MLP was able to model the relationship between Leptospirosis incidence and factors better than GWR. Additionally, results of both GWR and MLP models showed that average humidity and annual rainfall are most important affecting factors on Leptospirosis incidence in the Northern provinces of Iran, respectively. Such predictive maps can be used to provide essential guidelines for planning of effective control strategies and identification of high risk areas of Leptospirosis which should receive more preventive measures from policy makers and healthcare authorities.

Recent developments in open source digital elevation models (DEMs) increase their applications in different disciplines. The lack of proper information about the accuracy of these models in regions with different topographic characteristics, make their analysis unreliable. Most estimates of accuracy, e.g., mean, standard deviation and RMSE depend on an implicit assumption that the errors comprise a random sample from a normal distribution. But, analytic data often depart from that assumption. Therefore, this paper first study statistical characteristics of SRTM and GDEM open source global DEMs. Then we define a confidence interval for RMSE which make it possible to investigate the appropriateness of reference points. Afterwards, measures for accuracy assessment of DEMs based on robust methods in L1 norm and Huber's method as well as conventional methods in L2 norm are discussed. In order to consider different topographic characteristics we compare SRTM and GDEM based on the defined measures in Urban, Flat, Hill and Mountain regions. Finally, we studied how the errors propagate into slope and aspect maps. It was found that although GDEM has better resolution, but SRTM performs better based on the defined measures in all the regions. It is also illustrated that the accuracy of both models in urban are better than other regions.

Nowadays, Earth observation (EO) technology became an indispensable tool to help environmental monitoring, as well as their changes, for natural resources management, urban planning and development, water management and land use planning. In particular, radar EOs, unlike the optical ones, can be collected regardless of illumination and weather conditions. Multitemporal polarimetric synthetic aperture radar (PolSAR) images are useful source of information for detection and mapping the environmental changes, especially in wide areas, during the day and night and all weather conditions. Change detection methods can identify the change or no change conditions in land covers using the time series observations. In this paper a method is proposed for change detection in SAR remote sensing images. This method is based on the Change Point Analysis. The cumulative frequency of difference image, which contains the environmental changes, normally follows a specific class of statistical distribution. Gaussian mixture model is one of the most suitable models for Change Point Analysis. This model can efficiently estimate the parameters of mixture distribution. The intersection point of two distributions is a change point, which can be seen as a threshold. This threshold is then used to separate the change and no change classes. The proposed method is implemented and analyzed using three SAR data sets. The analytical evaluations of the final change maps from two of these data sets with reference data had the Kappa coefficients of 90% and 96% respectively. The other data set contained the multitemporal PolSAR images and had been acquired over an agricultural area. The changes in these images were enough reliable to be connected to the agricultural activities, such as crop growing stages and harvesting, based on an available crop map. Finally, the method was evaluated against the Otsu method, as one of the best threshold estimation methods, and the results showed the superiority of the proposed method, e.g.2% better in term of kappa coefficient.. As a result, the proposed method, can be efficiently employed for land cover change detection and monition in natural resources management.

Forest dieback is a complex and important phenomenon that happened in the world’s most oak forests nearly a century ago. In recent years, this has occurred in ZAGROS oak forests due to successive droughts. ZAGROS forest region, with an area of about 6 million hectares including provineces West Azerbaijan, Kurdistan, Kermanshah, Ilam, Lorestan, Khuzestan, Fars, Esfahan, Chaharmahal and Bakhtiari, Kohgiluyeh and Boyer-Ahmad, Hamedan is exposed to several threats. Forests of ILAM province are part of ZAGROS forests, which are located in the west part of the mountain chain. The phenomenon of forest dieback has been seen in this area during recent years. In this study the dieback of forests in ILAM province area has been studied using Landsat satellite imagery and Google earth images, meteorological data and information of the amount of dust in province atmosphere. This study has conducted over a period of 15 years. Also we have to mention that the dust data have been extracted from 550 nm band Modis satellites images. In order to detect vegetation and forest area of ILAM, vegetation indices have been used in Landsat satellite images. In this study, the five vegetation indices, NDVI (Normalized Difference Vegetation Index), EVI (Enhanced Vegetation Index), RVI (Relative Vigor Index), SAVI (Soil Adjusted Vegetation Index), ARVI (Atmospherically Resistant Vegetation Index) have been used. Also to determine the appropriate vegetation index for detecting the changes in vegetation coverage areas, these indicators have been compared to each other. In order to compare these indicators, Google earth imagery has been used. Also with the use of Google earth images, the vegetation coverage of 200 pixels has been estimated in Landsat satellite images. By using these 200 pixels we compared the vegetation indices and carefully calculated the accuracy of each one. In this study, the generalized correlation of the indices with the vegetation coverage changes is respectively: 0.939, 0.953, 0.945, 0.914, 0.925 and according to these results, EVI vegetation index is used as the preferable indicator for vegetation change detection index in ILAMs forests. Landsat Images were obtained for each year and the vegetation changes amount was calculated towards the previous year for each year. Using the rainfall data index SPI (Standardized Precipitation Index), in these periods of one year, nine-month and three-month. The correlation between EVI index annual changes examined using SPI index the results were respectively, 0.43, 0.76, 0.09. In this paper, we used PCA (Principal Component Analysis) method to detecte the changed areas in forest vegetation coverage. The rate of changes in pixels amount in each year is calculated respect to year 1379. Also in order to separate the forest area changes from others, the behaviour of each pixel is studied during a period of 15 years. Also with the introduction of two patterns and determination of correlation between the patterns change behaviour, ILAMs vegetation coverage changed areas were isolated from other changes, and by deleting the non–changed pixels, the average annual change was obtained for ILAM province forest. Average entered dust was calculated for each year, using meteorological and Modis satellite images data. Also by applying a two–parameter linear regression, the combined impact of two factors, rainfall and dust was determined. The study implied that precipitation is the most effective parameter on dieback. The influence of two factors, rainfall and dust are respectively 62% and 38%.

Repre sentation of the user's environment along with functionalities like route finding and exploring point of interests (POIs), is usually provided in the form of 2D maps which limit the user to a top-down view of the nearby environment. Considering the 3D form of real-world objects, the presentation of them as 3D city model plays an important role in visualization and location-based functionalities. In the visualization part, 3D city model helps users to identify real-world objects by making a virtual environment and regarding the geometric attributes of them. Furthermore, 3D city model is utilized in variety of applications such as visibility analysis, energy demand estimation, urban facility management, 3D cadastre and indoor navigation. Recent improvements in the hardware and software specifications of smart phones along with the utilization of powerful CPUs and GPUs provided the developers with the ability of exploiting mobile devices as platforms for different location-based usages. Virtual Reality (VR) is a technology that simulates objects of the real world in a virtual environment and enables users to interact with those objects.3D city models that contain elements like buildings, vegetation areas, roads and terrain, are the examples of VR applications in smart phones and tablets. This paper describes a mobile-based VR application to represent and explore campus objects exists in the faculty of Geodesy and Geomatics Engineering of K.N.Toosi University of technology. Generally, OpenGL ES API was used to render 2D and 3D graphic objects in mobile devices and hardware accelerated by using GPU. In spite of that, 3D city model elements like buildings are complicated objects, which produce in desktop graphic softwares such as Google Sketchup, City Engine, 3ds MAX. A variety of game engines and graphic libraries are developed to load and render graphic models based on the low-level structure of OpenGL ES. These libraries provide functionalities like loading and creating objects, supporting different graphic formats, collision detection and so on. Our mobile application is developed based on Libgdx cross-platform game engine.3D models of campus objects are produced in Google Sketchup software form 2D footprint of buildings in 1: 2000 aerial map. In the next step, models were textured and their coordinates and non-spatial attributes were stored in a KML file format. The serialization process was performed in the mobile device to construct and render models in graphic environment. In this study, we have implemented two types of representation and interaction with 3D city model: 1) Bird' s-eye view and 2) First-person view. In the Bird's eye view, user is elevated above the surface and control buttons and touch screen events handle user' s interactions. On the other hand, the first-person view is mimic of Augmented Reality (AR) view and user can walk in the environment and explore nearby objects. In this approach, the position and orientation of the mobile device have determined by sensory data and the low-pass filter has been utilized to decrease the noise of the data. In order to represent non-spatial information of objects, we have utilized frustum-culling concept to determine the selected model by the user and retrieved corresponding attributes. For that, we created a minimum bounding box (MMB) of each model and intersected pointing vector of the observer with MMBs to select nearest models to the user. The mixed interactive approach of our research enables users to explore their nearby urban environment in two different view and gets related attribute information of models directly.