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The increasing utilization of Volunteered Geospatial Information (VGI), including data from OpenStreetMap (OSM), raises concerns regarding data quality due to variations in contributors' skills and tools. This study evaluates the positional accuracy of voluntary features in Tehran by comparing them with official datasets. A feature matching approach, employing Hausdorff distance, orientation difference, and buffer overlap--normalized through fuzzy logic--was utilized to assess accuracy. Preprocessing steps included standardizing data extent and coordinate systems, correcting topological errors, and converting datasets into graph structures. The results indicate that most voluntary features exhibited high positional accuracy, with over 87% achieving positional accuracy above 82%. Temporal analysis revealed peaks in voluntary contributions in 2012 and 2017; however, there was a slight overall decline in positional accuracy from 2007 to 2022, as indicated by a negative trend line slope of - 0.001834. This study introduces a method for assessing the accuracy of historical data using feature matching across a large area, such as Tehran, to track trends in positional accuracy over time. It emphasizes the necessity for extrinsic assessment in VGI, noting that technological advancements do not always correlate with improved positional accuracy. The comprehensive approach presented in this study provides valuable insights into the quality and reliability of VGI. [ABSTRACT FROM AUTHOR]

Indoor positioning is one of the most challenging issues in location-based services. Smartphone-based Pedestrian Dead Reckoning (PDR) is commonly used as an indoor positioning system because it does not require infrastructure. The positioning estimation errors, however, are cumulatively increased over time. In this approach, step length estimation error is one of the main sources of positioning error. In this study, to improve indoor positioning accuracy using smartphone-embedded sensors, the pedestrian's gender and walking speed are considered effective factors in adjusting the parameters of step length estimation methods. Accordingly, collected data are divided into six classes based on walking speed (high, medium, low) and gender (female, male). K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Decision Trees (DTree) algorithms are adopted for classification. The classification accuracy of each of the KNN, SVM, and DTree algorithms are 93.4%, 92.4%, and 76.6% respectively. Moreover, the peak detection method is applied to identify the pedestrian's steps, and Weinberg and Ladetto methods are adopted to estimate step length. Step detection accuracy was 99.015%. Also, the error of step length estimations using Weinberg and Ladetto methods are 2.48% and 1.95%, respectively. In addition, the Extended Kalman Filter (EKF) filter is used for heading estimation, and fast walking results in the highest heading estimation error for both males and females. The mean and STD of the heading estimation error using EKF algorithms are 2.97 degrees and 2.99 degrees, respectively. In the final, the pedestrian's position is estimated according to the PDR method using estimated step lengths and estimated headings. Along a 25.8-meter path, using the Weinberg method for step length estimation, the average absolute and relative positioning errors are 0.76 and 2.95%, respectively. Moreover, using Ladetto s method for step length estimation, the average absolute and relative positioning errors are 0.92 and 3.57%, respectively. [ABSTRACT FROM AUTHOR]

The crowdsourced information in this kind of project results from numerous and not organized volunteers, making verification of the quality of their contributions a challenge. Several studies have emphasized using indirect and intrinsic measures to assess the quality of VGI in literature. Moreover, measuring the contributor’s reputation score as one of the intrinsic factors for determining whether their produced information is reliable and credible remains an open issue. This study first introduces and discusses the most effective interactions on the reputation of VGI contributors in detail and visually. Then attempts to propose a comprehensive reputation system based on various implicit evaluations among volunteers in their contribution process, depicting their performance. This system is successfully applied in linear features in study areas, and the results are shown graphically. Finally, two other existing frameworks are implemented on desired data for comparison purposes, and the results are discussed. [ABSTRACT FROM AUTHOR]

One of the most widely used interpolation methods is the models based on radial basis functions (RBFs) to achieve global and exact surfaces. RBFNN model has been used as an interpolation method in this research to deal with problems in the RBF interpolation method such as the surface fluctuations between sample points and high complexity. Therefore, the centers, radii, and weights of the RBFs were optimized using the PSO algorithm. The centers and radii of RBFs have been initialized using K-means clustering and the K-Nearest Neighbor method respectively. Moreover, the weights of RBFs have been calculated using a pseudo-inverse method. To evaluate the effectiveness of the proposed algorithm the interpolation process has been done on three sets of points with the irregular distribution with different elevation patterns. The results show that MQ and IQ functions provide better accuracy than the GA function in flat areas with low elevation changes as well as areas with average elevation changes. Furthermore, the MQ function could be more accurate than IQ and GA functions in areas with large elevation changes. [ABSTRACT FROM AUTHOR]

Earthquake hazards cause changes in landforms, economic losses, and human casualties. Seismic Vulnerability Mapping (SVM) is key information to prevent and predict the damage of earthquakes. The purpose of this study is to train and compare the results of the Classification Tree Analysis (CTA) learner model with three Gini, Entropy, Ratio split algorithms, and Fuzzy ARTMAP (FAM) model by the development of hybrid models for SVM. The Seismic Vulnerability Conditioning Factors (SVCFs) such as environmental, physical, and social were selected using experts' opinions and experience. Thirteen factors were edited and prepared as the seismic vulnerability conditioning factors (SVCFs) used in this study. In order to seismic vulnerability mapping and models training, a database of training sites was created by the Multi-Criteria Decision Analysis-Multi-Criteria Evaluation (MCDA-MCE) hybrid process. Then, 70% of the points were used for training and 30% were used to validate the models' results based on the holdout method. Moreover, Relative Operating Characteristics (ROC), Seismic Relative Index (SRI), and Frequency Ratio (FR) were used to validate the results. The Area under the curve (AUC) for the algorithms Gini, Entropy, Ratio, and FAM model are 0.895, 0.890, 0.876, and 0.783, respectively. The results of the three validation methods show the highest performance for the Gini splitting algorithm. Accordingly, the percentage of social and physical vulnerability of Sanandaj city was determined based on the MCE-Gini optimal model: 27% of the area and 62% of the population of Sanandaj are under high vulnerability to earthquakes. So that, various factors such as worn urban texture, high population density and environmental factors were among the most important factors affecting seismic vulnerability. [ABSTRACT FROM AUTHOR]