نشریه مهندسی نقشه برداری و اطلاعات مکانی
سال نهم شماره 2 (پیاپی 34، خرداد 1397)

The precise monitoring of engineering structures is always one of the most important issues in engineering science.Monitoring engineering structures using traditional methods is very costly.Due to the high spatial resolution and low cost, radar acquisitions are considered as one of the most important tools for monitoring engineering structures. In this paper, the 3D displacement field of the Droodzan damwas calculated using radar acquisitions and geodetic data. Then, using existing observations and artificial neural network algorithm, the Dam deformation was predicted at a point until 2023.The correlation coefficients between the prediction model and the results obtained from the integration method for this point forthree dimensionswere 0.64-0.62 -0.77.RMSE of results for dimensions was obtained 7.87-8.54-5.12 mm. These results represent a good correlation between the predicted model and the results obtained from the integration method.

Nowadays, mobile phones have been increasingly advertised. These performance advertisement tools altered to be one of the beneficial factor in order to promote products and services in national or local companies. One of the outstanding features of mobile phones is that everybody has its accessibility in different circumstances and times. Companies struggle to draw customers attention by providing information, stimulating text or image to advertise their products by which high cost have been consumed. In this study, a system is designed and implemented for efficient and effective interaction between companies and customers. It is worth mentioning here this system has some great feature like being aware of text, owning mobile user Interface and presenting location-based service. These features enable companies to design an advertisement in a purposeful way. Such these advertisements can effectively be sent to the population company which are in target. Finally, the system was evaluated. Reduction in cost and effectiveness of advertising are grounded in the result of study.

Emissivity is one of the important physical properties of the Earth's surface and atmosphere. The knowledge of emissivity plays a valuable role in environment studies, urban climate, evapotranspiration, vegetation assessment, heat flux estimation, hydrological cycle, meteorology, and the interactions between land surface and atmosphere. Also, in order to accurate estimate of Land Surface Temperature, we need to obtain the emissivity. All of the emissivity estimating methods have been divided to semi empirical, Physical and temperature and emissivity separation methods. The aim of this study is to compare physically based methods of emissivity retrieval and finally obtain emissivity. This research consists of 6 main sections: At the first, the noise and water vapor absorption bands of HyTES hyperspectral image were removed, then 202 optimal bands were selected. In the next step, Approximate Expression of the Channel Radiance algorithm was applied. Then, other physically based algorithm that include Reference Channel Method, Emissivity Normalization Method, Emissivity Renormalization Method and Temperature-Independent Spectral Indices were applied. In order to evaluate these methods and for quality assessment, we used the satellite products that have been prepared by NASA. The results of the quality assessment show that RMSE for Approximate Expression of the Channel Radiance, Reference Channel, Temperature-Independent Spectral Indices, Emissivity Normalization Method and Renormalization Methods for band 180 are 0.05, 0.03, 0.05, 0.05 and 0.06 respectively.

Building extraction from different data resources, which can be applied in 3D city modeling, is one of the most growing research topics in Photogrammetry and Remote Sensing. The aim of this study is proposing a new method for extracting buildings from LiDAR point clouds using a fuzzy inference system. In the proposed method, first, a pre-processing step is performed to eliminate low altitude points and also noisy points. In this regard, an optimal noise removal technique is used to remove noise and outliers from LiDAR point clouds. The proposed method can almost eliminate all of the noises and outliers from the point clouds. After pre-processing step, only high altitude points in the form of points groups including buildings and trees will remain. Then, due to the uncertainty in distinguishing building groups from tree groups, a fuzzy inference system is designed and implemented. In the proposed fuzzy system, three geometric descriptors of "SumD", "Area" and "Volume" are considered as input variables, and feature type as output variable is defined. In order to evaluate the proposed method, a test area of Belgium is used, and the obtained results proved the ability of the proposed fuzzy inference system in resolving the uncertainty in detecting building points group. Moreover, the proposed noise removal method increased 10% in accuracy and improving the quality of the fuzzy system.
3D city Modeling using the technology of LiDAR is one of the most growing research topics in Photogrammetry and Remote Sensing.

Nowadays, automatic detection and recognition of traffic signs is one of the interest topics to researchers. Researches rely on the automatic extraction of the signs with the desirable speed and accuracy. The algorithms are developed in such a way that the system can detect and recognize traffic signs in a highly complex urban environment. A lot of research has been done on this issue in recent decades, but with this extensive research, this topic is still attractive to researchers for future research. The reason is the existence of some challenges that reduce the accuracy of existing algorithms. These are intensive changes in light condition, shadow, the existence of obstacles, scale and orientation changes, the existence of a complex field, the speed of the algorithm, and the cheapness of the system.
Generally, the process of detecting and identifying traffic signs is performed in three separate sections, consisting of image segmentation, detection and recognition of signs. In each of these processing steps, the above challenges may cause the identification process to be difficult. For example, in the segmentation step, the intense light changes of the imaging environment affect the processing results. Also, in detection step, the presence of obstacle against the signs in the accrued images cased Detection of the shape of the sign is difficult. Hence, in this paper, an overview of the existing methods for each of the three steps of detection of traffic signs and also their challenges are discussed.

The primary routing,done by landmarks, celestial objects and magnetic compasses.With Advancements in science and technology today, inertial sensors and advanced satellite systems and ultrasound waves are used for positioning.Navigation systems for the vehicle, is a technology, which today has become more widespread,With the increased small portable technologies,A new group of users, ie pedestrians have been added to the users of navigation systems,Pedestrian navigation is a fascinating subject,So include a wide scope of applications and a large number of potential users.Currently, there is a great deal of research in this area and many commercial products have been developed.Due to the unpredictable movement of people and environments that are somehow involved with them,Quality pedestrian navigation services can not simply be guaranteed everywhere.Certainly for navigation in open environments,Technology is the first satellite positioning systemsbutin enclosed areas or in urban valleys the situation is more complicated and more difficult(Buildings and human infrastructure, satellite signals weaken and are blockages).For solving this problem, methods have been proposed for pedestrian navigationIn this study, have presented new methods of positioning pedestrians, including positioning with wireless waves,Lamps LED (visible light communication), integration of systems, inertial sensors, Map-matching algorithms and Strategies to improve the accuracy of positioning.And accordingly could, chose the most efficient method to suit your needs.

The Geospatial data production from different sources with varied precision and scales has experienced an extensive growth by increase of geospatial data usage in daily life. Through these diverse datasets in some of the applications, there is a need to identify corresponding features by geospatial data matching problems. In other words, the goal of geospatial data matching is to identify corresponding features which are the identical entities in real world, but they may be represented by different geometries, scales, precisions, and spatial relations in different datasets. The use of geospatial data matching problems in different studies like data conflation or authoritative data enrichment demonstrates the significant importance of such these problems in different fields of geospatial science. In general, feature matching problems can be classified into point matching, linear matching, and polygon matching based on the type of spatial feature applied in the matching process. Most of the previous studies have focused on linear matching. Therefore, this study is based on the polygon features which are more complex in comparison with the linear features, and most of recent studies have focused on this class. In this paper, the polygon matching problems have been investigated in three aspects of similarity measures, matching algorithms, and corresponding relations by comprehensive and precise study of previous work. Furthermore, the classifications in each aspect and the advantages and disadvantages of different methods have been presented.