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Alireza Chehreghan
Faculty of Mining Engineering, Sahand University of Technology, Tabriz, Iran

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Research article
Published: 29 July 2021 in Earth Science Informatics
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Volunteered geographic information (VGI) is a large and up-to-date data source, which is available to the public easily. VGI enables public participation by leveraging scientific research and ease of data entry. OpenStreetMap (OSM) is one of the most popular examples of a volunteered geographic information project that has turned into a major source as the substitution of geographical data over the past years. Because OSM data quality is very variable, its various aspects have been investigated in previous studies. Assessing the reliability of volunteered geographic data has been a topic of interest to researchers during recent years. The objective of this study is to introduce an approach for computing the reliability indicators as tools for assessing OSM data quality using the history of data. To prepare the data required, the history file of the OSM dataset for the study region was extracted. Then, historical data cleaning was carried out by identifying and eliminating the outlier data. Afterward, the reliability indicator was calculated through criteria such as the number of versions, the number of user participation, temporal variations, and the number of tags editing. In the last step, to evaluate the proposed approach in calculating the reliability indicator, the level of feature reliability was compared with their spatial accuracy calculated via feature matching of the OSM and official data. The results show among 7478 reliability features of the OSM, approximately 4338 feature involves reliability of above 50%, containing 58.01% of the datasets, and among 5659 matching features of the OSM dataset, 4429 features have a similarity percentage of above 70%, containing 78.26% of the datasets. Increasing the number of versions, the number of users, and the temporal variation range of a route increased the reliability. Contrastingly, tag editing reduces reliability. Moreover, according to the results, a correlation coefficient of 0.695 between the reliability and spatial accuracy indicates a direct relationship of reliability in the quality of the OSM dataset.

ACS Style

Najmeh Teimoory; Rahim Ali Abbaspour; Alireza Chehreghan. Reliability extracted from the history file as an intrinsic indicator for assessing the quality of OpenStreetMap. Earth Science Informatics 2021, 14, 1413 -1432.

AMA Style

Najmeh Teimoory, Rahim Ali Abbaspour, Alireza Chehreghan. Reliability extracted from the history file as an intrinsic indicator for assessing the quality of OpenStreetMap. Earth Science Informatics. 2021; 14 (3):1413-1432.

Chicago/Turabian Style

Najmeh Teimoory; Rahim Ali Abbaspour; Alireza Chehreghan. 2021. "Reliability extracted from the history file as an intrinsic indicator for assessing the quality of OpenStreetMap." Earth Science Informatics 14, no. 3: 1413-1432.

Research article
Published: 26 March 2021 in Transactions in GIS
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The continuous development of positioning technologies and computing solutions for the integration of large trajectory data sets offers many novel research opportunities. Among various research domains, the extraction of users' movement patterns is an important issue that is yet to be addressed. While many previous studies have analyzed human and animal movements from a predominantly geometrical point of view, additional semantics are still required to provide a better understanding of the patterns that emerge. User activity data provide important information resources to analyze and predict movement patterns in urban environments. This study introduces a computational framework that combines the geometric and activity‐based dimensions of human trajectories. First, the geometrical dimension considers a series of parameters (i.e., turning points, curvature, and self‐intersection) that are extracted by a convex‐hull algorithm and characterizes a given trajectory. Second, user activity transitions are modeled and then denote some recurrent patterns. Finally, geometric and activity patterns are integrated into a unified trajectory modeling framework. This favors the analysis of human movement patterns by taking into account the geometric and activity dimensions. The entire approach and framework have experimented with the LifeMap Korean trajectory data set commonly considered as a reference benchmark. The experiments showed how the integration of geometrical and activity‐based dimensions could provide a better understanding of the patterns and trends that emerge from a large trajectory data set.

ACS Style

Amin Hosseinpoor Milaghardan; Rahim Ali Abbaspour; Christophe Claramunt; Alireza Chehreghan. An activity‐based framework for detecting human movement patterns in an urban environment. Transactions in GIS 2021, 1 .

AMA Style

Amin Hosseinpoor Milaghardan, Rahim Ali Abbaspour, Christophe Claramunt, Alireza Chehreghan. An activity‐based framework for detecting human movement patterns in an urban environment. Transactions in GIS. 2021; ():1.

Chicago/Turabian Style

Amin Hosseinpoor Milaghardan; Rahim Ali Abbaspour; Christophe Claramunt; Alireza Chehreghan. 2021. "An activity‐based framework for detecting human movement patterns in an urban environment." Transactions in GIS , no. : 1.

Articles
Published: 02 October 2019 in Annals of GIS
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Spatiotemporal movement pattern discovery has stimulated considerable interest due to its numerous applications, including data analysis, machine learning, data segmentation, data reduction, abnormal behaviour detection, noise filtering, and pattern recognition. Trajectory clustering is among the most widely used approaches of extracting interesting patterns in large trajectory datasets. In this paper, regarding the optimal performance of density-based clustering, we present a comparison between eight similarity measures in density-based clustering of moving objects’ trajectories. In particular, Distance Functions such as Euclidean, L1, Hausdorff, Fréchet, Dynamic Time Warping (DTW), Longest Common SubSequence (LCSS), Edit Distance on Real signals (EDR), and Edit distance with Real Penalty (ERP) are applied in DBSCAN on three different datasets with varying characteristics. Also, experimental results are evaluated using both internal and external indices. Furthermore, we propose two modified validation measures for density-based trajectory clustering, which can deal with arbitrarily shaped clusters with different densities and sizes. These proposed measures were aimed at evaluating trajectory clusters effectively in both spatial and spatio-temporal aspects. The evaluation results show that choosing an appropriate Distance Function is dependent on data and its movement parameters. However, in total, Euclidean distance proves to show superiority over the other Distance Functions regarding the Purity index and EDR distance can provide better performance in terms of spatial and spatio-temporal quality of clusters. Finally, in terms of computation time and scalability, Euclidean, L1, and LCSS are the most efficient Distance Functions.

ACS Style

A. Moayedi; R. Ali Abbaspour; A. Chehreghan. An evaluation of the efficiency of similarity functions in density-based clustering of spatial trajectories. Annals of GIS 2019, 25, 313 -327.

AMA Style

A. Moayedi, R. Ali Abbaspour, A. Chehreghan. An evaluation of the efficiency of similarity functions in density-based clustering of spatial trajectories. Annals of GIS. 2019; 25 (4):313-327.

Chicago/Turabian Style

A. Moayedi; R. Ali Abbaspour; A. Chehreghan. 2019. "An evaluation of the efficiency of similarity functions in density-based clustering of spatial trajectories." Annals of GIS 25, no. 4: 313-327.

Research articles
Published: 11 August 2018 in International Journal of Image and Data Fusion
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Evaluation of the quality of volunteered geographic information (VGI) has been the subject of a plethora of research in recent years as an imperative issues. In this paper, the corresponding objects between data sets of OpenStreetMap, as one of the well-known VGI projects, and the reference data sets are identified based on an automatic linear object matching method. Moreover, to identify more corresponding objects in two data sets and measuring the completeness more accurately, geometric properties are used. The results showed that 92% of the objects of the OSM data set matched to the reference data set and the total length of the matched objects was 87% of the total length of the objects. The corresponding objects in two data sets have average of 0.86° of spatial similarity. By examining the objects of the OSM data set from 2013 to 2017, a rise of 87.2% detected in individuals’ participation in creating the objects and the average spatial similarity degree also underwent an improvement of 0.15.

ACS Style

Alireza Chehreghan; Rahim Ali Abbaspour. An evaluation of data completeness of VGI through geometric similarity assessment. International Journal of Image and Data Fusion 2018, 9, 319 -337.

AMA Style

Alireza Chehreghan, Rahim Ali Abbaspour. An evaluation of data completeness of VGI through geometric similarity assessment. International Journal of Image and Data Fusion. 2018; 9 (4):319-337.

Chicago/Turabian Style

Alireza Chehreghan; Rahim Ali Abbaspour. 2018. "An evaluation of data completeness of VGI through geometric similarity assessment." International Journal of Image and Data Fusion 9, no. 4: 319-337.

Journal article
Published: 28 June 2018 in ISPRS International Journal of Geo-Information
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OpenStreetMap (OSM) has proven to serve as a promising free global encyclopedia of maps with an increasing popularity across different user communities and research bodies. One of the unique characteristics of OSM has been the availability of the full history of users’ contributions, which can leverage our quality control mechanisms through exploiting the history of contributions. Since this aspect of contributions (i.e., historical contributions) has been neglected in the literature, this study aims at presenting a novel approach for improving the positional accuracy and completeness of the OSM road network. To do so, we present a five-stage approach based on a Voronoi diagram that leads to improving the positional accuracy and completeness of the OSM road network. In the first stage, the OSM data history file is retrieved and in the second stage, the corresponding data elements for each object in the historical versions are identified. In the third stage, data cleaning on the historical datasets is carried out in order to identify outliers and remove them accordingly. In the fourth stage, through applying the Voronoi diagram method, one representative version for each set of historical versions is extracted. In the final stage, through examining the spatial relations for each object in the history file, the topology of the target object is enhanced. As per validation, a comparison between the latest version of the OSM data and the result of our approach against a reference dataset is carried out. Given a case study in Tehran, our findings reveal that the completeness and positional precision of OSM features can be improved up to 14%. Our conclusions draw attention to the exploitation of the historical archive of the contributions in OSM as an intrinsic quality indicator.

ACS Style

Afsaneh Nasiri; Rahim Ali Abbaspour; Alireza Chehreghan; Jamal Jokar Arsanjani. Improving the Quality of Citizen Contributed Geodata through Their Historical Contributions: The Case of the Road Network in OpenStreetMap. ISPRS International Journal of Geo-Information 2018, 7, 253 .

AMA Style

Afsaneh Nasiri, Rahim Ali Abbaspour, Alireza Chehreghan, Jamal Jokar Arsanjani. Improving the Quality of Citizen Contributed Geodata through Their Historical Contributions: The Case of the Road Network in OpenStreetMap. ISPRS International Journal of Geo-Information. 2018; 7 (7):253.

Chicago/Turabian Style

Afsaneh Nasiri; Rahim Ali Abbaspour; Alireza Chehreghan; Jamal Jokar Arsanjani. 2018. "Improving the Quality of Citizen Contributed Geodata through Their Historical Contributions: The Case of the Road Network in OpenStreetMap." ISPRS International Journal of Geo-Information 7, no. 7: 253.

Journal article
Published: 22 September 2017 in Geodesy and cartography
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One of the main steps of acquiring and handling data in a multi-scale database is generation of automatic links between corresponding objects in different scales, which is provided by matching them in the datasets. The basic concept of this process is to detect and measure the spatial similarity between various objects, which differ from one application to another, largely depends on the intrinsic properties of the input data. In fact, spatial similarity index, which is a function of other criteria such as geometric, topological, and semantic ones, is to some extent uncertain. Therefore, the present study aims to provide a matching algorithm based on fuzzy reasoning, while considering human spatial cognition. The proposed algorithm runs on two road datasets of Yazd city in Iran, which are in the scales of 1:5000 and 1:25000. The evaluation results show that matching rate and correctness of the algorithm is 92.7% and 88%, respectively, which validates the appropriate function of the proposed algorithm in matching.

ACS Style

Ali Dehghani; Alireza Chehreghan; Rahim Ali Abbaspour. MATCHING OF URBAN PATHWAYS IN A MULTI-SCALE DATABASE USING FUZZY REASONING. Geodesy and cartography 2017, 43, 92 -104.

AMA Style

Ali Dehghani, Alireza Chehreghan, Rahim Ali Abbaspour. MATCHING OF URBAN PATHWAYS IN A MULTI-SCALE DATABASE USING FUZZY REASONING. Geodesy and cartography. 2017; 43 (3):92-104.

Chicago/Turabian Style

Ali Dehghani; Alireza Chehreghan; Rahim Ali Abbaspour. 2017. "MATCHING OF URBAN PATHWAYS IN A MULTI-SCALE DATABASE USING FUZZY REASONING." Geodesy and cartography 43, no. 3: 92-104.

Research article
Published: 23 August 2017 in International Journal of Image and Data Fusion
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Distance in geospatial sciences has many applications, including the calculation of spatial similarity degree in object-matching problems. Various distances have so far been utilised for this purpose. However, no study has examined the efficiency of methods used for finding solutions for linear object matching in data sets with different or the same scales and sources. The present study investigated the efficiency of the most important and applicable spatial distances (13 types of distance methods) in vector data sets with different scales and sources. To this end, we employed three data sets of urban roads network of different sources with the scales of 1:2000, 1:5000 and 1:25,000. In the considered approach, the data sets are initially pre-processed to unify the format and coordinate system as well as removing topological errors. The corresponding objects in the data sets are then identified, and one-to-null, null-to-one, one-to-one, one-to-many, many-to-one and many-to-many relations are extracted. Ultimately, the method with the minimum dispersion in calculation of the distances between corresponding objects is selected as the efficient method. The results indicated that the short-line median and mean Hausdorff methods achieved higher efficiencies compared to the other employed methods. In addition to achieving a smaller variance compared to other introduced methods, these two methods are well capable of identifying one-to-many (many-to-one) and many-to-many relations.

ACS Style

Alireza Chehreghan; Rahim Ali Abbaspour. An assessment of the efficiency of spatial distances in linear object matching on multi-scale, multi-source maps. International Journal of Image and Data Fusion 2017, 9, 95 -114.

AMA Style

Alireza Chehreghan, Rahim Ali Abbaspour. An assessment of the efficiency of spatial distances in linear object matching on multi-scale, multi-source maps. International Journal of Image and Data Fusion. 2017; 9 (2):95-114.

Chicago/Turabian Style

Alireza Chehreghan; Rahim Ali Abbaspour. 2017. "An assessment of the efficiency of spatial distances in linear object matching on multi-scale, multi-source maps." International Journal of Image and Data Fusion 9, no. 2: 95-114.

Original article
Published: 29 July 2017 in Modeling Earth Systems and Environment
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The purpose of object matching is to identify corresponding objects in various datasets. This article provides a Geometric-based Matching framework based on the Optimization Approach, called GeMOA, for improvement of linear object matching in the datasets with different scales, sources, and production time. GeMOA performs object matching in different datasets, while considering only the extracted geometric criteria from the objects, removes any initial dependency on common empirical parameters such as threshold for spatial similarity degree, buffer distance, and weights of the criteria. Moreover, The proposed solution considers all existing relations between the objects, including 1:0, 0:1, 1:1, 1:N, N:1, and M:N. For assessment of GeMOA efficiency, three datasets of scales: 1:2000, 1:5000, and 1:25000, with different sources and collections time. The results show that GeMOA, contrary to many previous methods, does not lose its applicability in confronting datasets with different scales and sources, and attaints a better than 90% F-score.

ACS Style

Alireza Chehreghan; Rahim Ali Abbaspour. Estimation of empirical parameters in matching of linear vector datasets: an optimization approach. Modeling Earth Systems and Environment 2017, 3, 1029 -1043.

AMA Style

Alireza Chehreghan, Rahim Ali Abbaspour. Estimation of empirical parameters in matching of linear vector datasets: an optimization approach. Modeling Earth Systems and Environment. 2017; 3 (3):1029-1043.

Chicago/Turabian Style

Alireza Chehreghan; Rahim Ali Abbaspour. 2017. "Estimation of empirical parameters in matching of linear vector datasets: an optimization approach." Modeling Earth Systems and Environment 3, no. 3: 1029-1043.

Journal article
Published: 09 June 2017 in GIScience & Remote Sensing
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ACS Style

Alireza Chehreghan; Rahim Ali Abbaspour. A new descriptor for improving geometric-based matching of linear objects on multi-scale datasets. GIScience & Remote Sensing 2017, 54, 836 -861.

AMA Style

Alireza Chehreghan, Rahim Ali Abbaspour. A new descriptor for improving geometric-based matching of linear objects on multi-scale datasets. GIScience & Remote Sensing. 2017; 54 (6):836-861.

Chicago/Turabian Style

Alireza Chehreghan; Rahim Ali Abbaspour. 2017. "A new descriptor for improving geometric-based matching of linear objects on multi-scale datasets." GIScience & Remote Sensing 54, no. 6: 836-861.

Article
Published: 08 June 2017 in Spatial Information Research
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Owing to increasing number of moving objects and positioning technologies both indoors and outdoors, spatial databases have encountered immense amount of spatio-temporal data. This causes spatial analyses of trajectories to be one of the most interesting topics in GIS research recently. Finding similar trajectories are of high importance to utilize these analyses. Distance functions play the key role in similarity measurement to find similar trajectories. Thus far, various distance functions have been proposed, but they show some drawbacks for spatial trajectories due to their nature. In this paper, a novel method is proposed considering spatio-temporal behaviors of spatial trajectories. In first step, polyline with minimum number of line segments representing trajectory in correspondent space with acceptable similarity is fitted. Then, the comparisons of trajectories are based on fitted polylines. Robustness of trajectories similarity to noise and sampling rate is proven with 96% according to evaluations.

ACS Style

Rahim Ali Abbaspour; Mohammad Shaeri; Alireza Chehreghan. A method for similarity measurement in spatial trajectories. Spatial Information Research 2017, 25, 491 -500.

AMA Style

Rahim Ali Abbaspour, Mohammad Shaeri, Alireza Chehreghan. A method for similarity measurement in spatial trajectories. Spatial Information Research. 2017; 25 (3):491-500.

Chicago/Turabian Style

Rahim Ali Abbaspour; Mohammad Shaeri; Alireza Chehreghan. 2017. "A method for similarity measurement in spatial trajectories." Spatial Information Research 25, no. 3: 491-500.

Article
Published: 06 June 2017 in Cartography and Geographic Information Science
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Object matching is used in various applications including conflation, data quality assessment, updating, and multi-scale analysis. The objective of matching is to identify objects referring to the same entity. This article aims to present an optimization-based linear object-matching approach in multi-scale, multi-source datasets. By taking into account geometric criteria, the proposed approach uses real coded genetic algorithm (RCGA) and sensitivity analysis to identify corresponding objects. Moreover, in this approach, any initial dependency on empirical parameters such as buffer distance, threshold of spatial similarity degree, and weights of criteria is eliminated and, instead, the optimal values for these parameters are calculated for each dataset. Volunteered geographical information (VGI) and authoritative data with different scales and sources were used to assess the efficiency of the proposed approach. According to the results, in addition to an efficient performance in various datasets, the proposed approach was able to appropriately identify the corresponding objects in these datasets by achieving higher F-Score.

ACS Style

Alireza Chehreghan; Rahim Ali Abbaspour. A geometric-based approach for road matching on multi-scale datasets using a genetic algorithm. Cartography and Geographic Information Science 2017, 45, 255 -269.

AMA Style

Alireza Chehreghan, Rahim Ali Abbaspour. A geometric-based approach for road matching on multi-scale datasets using a genetic algorithm. Cartography and Geographic Information Science. 2017; 45 (3):255-269.

Chicago/Turabian Style

Alireza Chehreghan; Rahim Ali Abbaspour. 2017. "A geometric-based approach for road matching on multi-scale datasets using a genetic algorithm." Cartography and Geographic Information Science 45, no. 3: 255-269.

Original articles
Published: 20 March 2017 in Geocarto International
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The study of objects similarity in terms of shape has various applications such as determining the similarity degree in object matching. To this end, different functions and descriptors have been used. However, efficiency of each method used in various studies for solving linear object matching in datasets with either different or similar scales or sources has not been studied yet. This article studies the efficiency of the most important functions (i.e. turning, signature, and tangent) along with shape descriptors (i.e. shape context, LORD, and shape signature) in vector datasets with different scales and sources. For this purpose, three datasets of roads network with different sources and scales were employed. Results showed the greater efficiency of the turning function compared to the other methods. In addition to being able to identify corresponding objects in multi-scale datasets, it can improve matching and is capable of discovering shape difference in non-corresponding objects.

ACS Style

Rahim Ali Abbaspour; Alireza Chehreghan; Amer Karimi. Assessing the efficiency of shape-based functions and descriptors in multi-scale matching of linear objects. Geocarto International 2017, 33, 879 -892.

AMA Style

Rahim Ali Abbaspour, Alireza Chehreghan, Amer Karimi. Assessing the efficiency of shape-based functions and descriptors in multi-scale matching of linear objects. Geocarto International. 2017; 33 (8):879-892.

Chicago/Turabian Style

Rahim Ali Abbaspour; Alireza Chehreghan; Amer Karimi. 2017. "Assessing the efficiency of shape-based functions and descriptors in multi-scale matching of linear objects." Geocarto International 33, no. 8: 879-892.

Original article
Published: 31 October 2016 in Modeling Earth Systems and Environment
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Landslide as one of the natural hazards has always caused huge financial losses and fatalities. Hence the goal of the present article is improvement in the prediction results of landslide occurrence in Tutkabon region in Gilan Province (Iran). For this purpose the Dempster–Shafer theory of evidence together with analyses and techniques of geospatial information systems (GIS) have been implemented for modeling and considering the uncertainties inherent in data selection. Also the parameters of slope, height, morphological conditions, earth curvature and distance to river and proximity to faults are taken as effective factors in landslide occurrence. Using the Dempster–Shafer theory, the belief, unbelief and uncertainty values for sub classes of each parameter are calculated separately and in continuation, utilizing the spatial information system, the landslide occurrence risk maps for each of these values are prepared at the study area. Finally for assessment of the results, the locations of landslide occurrence at the study area and the risk belief map are compared to each other. The results indicate that 65% of the landslides occur at the very high hazard class. Also assessing the results a value of AUC = 0.74 was obtained for the area under the prediction rate curve of the belief map.

ACS Style

Amin Hoseinpour Milaghardan; Mahmoudreza Delavar; Alireza Chehreghan. Uncertainty in landslide occurrence prediction using Dempster–Shafer theory. Modeling Earth Systems and Environment 2016, 2, 1 -10.

AMA Style

Amin Hoseinpour Milaghardan, Mahmoudreza Delavar, Alireza Chehreghan. Uncertainty in landslide occurrence prediction using Dempster–Shafer theory. Modeling Earth Systems and Environment. 2016; 2 (4):1-10.

Chicago/Turabian Style

Amin Hoseinpour Milaghardan; Mahmoudreza Delavar; Alireza Chehreghan. 2016. "Uncertainty in landslide occurrence prediction using Dempster–Shafer theory." Modeling Earth Systems and Environment 2, no. 4: 1-10.

Research article
Published: 17 September 2016 in Journal of the Indian Society of Remote Sensing
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The challenge of obtaining training data for supervised classifications of satellite images has led researchers to unsupervised algorithms, i.e. cluster analysis. Numerous researches have been conducted to improve quality and decrease uncertainty of results of this analysis. This study proposes a hybrid cost function as well as a hybrid clustering algorithm-Artificial Bee Colony optimization approach for the clustering of high-resolution satellite images. In order to evaluate viability of the proposed methodology, it is compared to some other classic clustering algorithms such as modified K-Means, K-Medoids, Fuzzy C-Means, and Kernel-based Fuzzy C-Means methods over three different study areas selected from a WorldView-2 satellite image. The Shannon entropy technique, Kappa coefficient, compactness, and separation criteria are used as quality and uncertainty indicators for the evaluation. The results of the study show that, compared to other methods, the hybrid algorithm obtained from the proposed cost function, Kernel-based Fuzzy C-Means method, and ABC algorithm provide clustering capabilities of higher quality and lower uncertainty levels.

ACS Style

Alireza Chehreghan; Rahim Ali Abbaspour. An Improvement on the Clustering of High-Resolution Satellite Images Using a Hybrid Algorithm. Journal of the Indian Society of Remote Sensing 2016, 45, 579 -590.

AMA Style

Alireza Chehreghan, Rahim Ali Abbaspour. An Improvement on the Clustering of High-Resolution Satellite Images Using a Hybrid Algorithm. Journal of the Indian Society of Remote Sensing. 2016; 45 (4):579-590.

Chicago/Turabian Style

Alireza Chehreghan; Rahim Ali Abbaspour. 2016. "An Improvement on the Clustering of High-Resolution Satellite Images Using a Hybrid Algorithm." Journal of the Indian Society of Remote Sensing 45, no. 4: 579-590.

Journal article
Published: 13 September 2016 in Annals of GIS
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ACS Style

Alireza Chehreghan; Mahmoodreza Delavar; Reza Zarei. An intelligent deployment method of geo-sensor networks in 3D environment. Annals of GIS 2016, 22, 301 -315.

AMA Style

Alireza Chehreghan, Mahmoodreza Delavar, Reza Zarei. An intelligent deployment method of geo-sensor networks in 3D environment. Annals of GIS. 2016; 22 (4):301-315.

Chicago/Turabian Style

Alireza Chehreghan; Mahmoodreza Delavar; Reza Zarei. 2016. "An intelligent deployment method of geo-sensor networks in 3D environment." Annals of GIS 22, no. 4: 301-315.

Journal article
Published: 17 March 2016 in Geocarto International
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ACS Style

Alireza Chehreghan; Rahim Ali Abbaspour. An assessment of spatial similarity degree between polylines on multi-scale, multi-source maps. Geocarto International 2016, 32, 471 -487.

AMA Style

Alireza Chehreghan, Rahim Ali Abbaspour. An assessment of spatial similarity degree between polylines on multi-scale, multi-source maps. Geocarto International. 2016; 32 (5):471-487.

Chicago/Turabian Style

Alireza Chehreghan; Rahim Ali Abbaspour. 2016. "An assessment of spatial similarity degree between polylines on multi-scale, multi-source maps." Geocarto International 32, no. 5: 471-487.

Journal article
Published: 22 October 2014 in The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Knowing the position has been an ambition in many areas such as science, military, business, etc. GPS was the realization of this wish in 1970s. Technological advances such as ubiquitous computing, as a conquering perspective, requires any service to work for any user, any place, anytime, and via any network. As GPS cannot provide services in indoor environments, many scientists began to develop indoor positioning systems (IPS). Smart phones penetrating our everyday lives were a great platform to host IPS applications. Sensors in smart phones were another big motive to develop IPS applications. Many researchers have been working on the topic developing various applications. However, the applications introduced lack simplicity. In other words, they need to install a step counter or smart phone on the ankle, which makes it awkward and inapplicable in many situations. In the current study, a new IPS methodology is introduced using only the usual embedded sensors in the smart phones. The robustness of this methodology cannot compete with those of the aforementioned approaches. The price paid for simplicity was decreasing robustness and complicating the methods and formulations. However, methods or tricks to harness the errors to an acceptable range are introduced as the future works.

ACS Style

S. Hassany Pazoky; A. Chehreghan; A. Sadeghi Niaraki; Rahim Ali Abbaspour. A NEW UBIQUITOUS-BASED INDOOR POSITIONING SYSTEM WITH MINIMUM EXTRA HARDWARE USING SMART PHONES. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 2014, XL-2/W3, 151 -155.

AMA Style

S. Hassany Pazoky, A. Chehreghan, A. Sadeghi Niaraki, Rahim Ali Abbaspour. A NEW UBIQUITOUS-BASED INDOOR POSITIONING SYSTEM WITH MINIMUM EXTRA HARDWARE USING SMART PHONES. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. 2014; XL-2/W3 ():151-155.

Chicago/Turabian Style

S. Hassany Pazoky; A. Chehreghan; A. Sadeghi Niaraki; Rahim Ali Abbaspour. 2014. "A NEW UBIQUITOUS-BASED INDOOR POSITIONING SYSTEM WITH MINIMUM EXTRA HARDWARE USING SMART PHONES." The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-2/W3, no. : 151-155.