Search Results (35)

Objective. This study examined the potential use of computed tomography panoramic mandibular indices on cone beam CT (CBCT) for assessing bone density in postmenopausal women with low bone mass. Study design. The study enrolled 104 postmenopausal women who underwent dual-energy X-ray absorptiometry (DXA) using a DXA scanner and mental foramen region CBCT alongside the NewTom VGi EVO Cone Beam 3D system. We assessed the relationship between the following DXA parameters: lumbar, femoral neck, and total hip T score, bone mineral density (BMD), and lumbar trabecular bone score (TBS). The following panoramic mandibular indices were also considered: the computed tomography mandibular index superior (CTI(S)), computed tomography mandibular index inferior (CTI(I)), and computed tomography mental index (CTMI). Results. The study revealed moderate correlations between CBCT indices and BMD/TBS scores: CTMI showed the highest correlation with the femoral neck T-score (r = 0.551, p < 0.0001). TBS scores were also moderately correlated with CBCT indices: CTMI showed a moderate positive correlation with TBS (r = 0.431, p < 0.0001); CTI(S) had a similar moderate positive correlation with TBS (r = 0.421, p < 0.0001). AUC values ranged from 0.697 to 0.733 for osteoporosis versus the osteopenia/normal group and from 0.734 to 0.744 for low versus normal bone quality groups, p < 0.0001. The comparison of the values of the studied indices between low versus normal bone quality (quantified with TBS) groups showed high sensitivity but low specificity. Conclusions. CBCT-measured indices CTI(S), CTI(I), and CTMI are useful in assessing patients with low bone mass to improve, by specific treatment, the prognosis of dental implants. Full article

OpenStreetMap (OSM) is among the most prominent Volunteered Geographic Information (VGI) initiatives, aiming to create a freely accessible world map. Despite its success, the data quality of OSM remains variable. This study begins by identifying the quality metrics proposed by earlier research to assess the quality of OSM building footprints. It then evaluates the quality of OSM building data from 2018 and 2023 for five cities within Québec, Canada. The analysis reveals a significant quality improvement over time. In 2018, the completeness of OSM building footprints in the examined cities averaged around 5%, while by 2023, it had increased to approximately 35%. However, this improvement was not evenly distributed. For example, Shawinigan saw its completeness surge from 2% to 99%. The study also finds that OSM contributors were more likely to digitize larger buildings before smaller ones. Positional accuracy saw enhancement, with the average error shrinking from 3.7 m in 2018 to 2.3 m in 2023. The average distance measure suggests a modest increase in shape accuracy over the same period. Overall, while the quality of OSM building footprints has indeed improved, this study shows that the extent of the improvement varied significantly across different cities. Shawinigan experienced a substantial increase in data quality compared to its counterparts. Full article

The spatial and semantic data of geographic addresses are extremely important for citizens, governments, and companies. The addresses can georeference environmental, economic, security, health, and demographic parameters in urban areas. Additionally, address components can be used by users to locate any point of interest (POI) with location-based systems (LBSs). For this reason, errors in address data can affect the geographic location of events, map representations, and spatial analyses. Thus, this paper presents the development of an algorithm for evaluating the quality of semantic and geographic information in any geospatial address dataset. The reference datasets are accessible using open data platforms or spatial data infrastructure (SDI) and volunteered geographic information (VGI), and both have been compared with commercial datasets using geocoding web services. Address quality analysis was developed using several open-source data science code libraries combined with spatial databases and geographic information systems. In addition, the quality of geographic addresses was evaluated by carrying out normalized tests in accordance with International Geospatial Standards (ISO 19157). Finally, this methodology assesses the quality of authorized and VGI address datasets that can be used for geocoding any relevant information in specific urban areas. Full article

The latest advances in spatial information technology have led to the emergence of Volunteered Geographic Information (VGI) as enrichment to existing spatial data sources. Additionally, Decision Support Systems (DSS) are among the fields that have seen major advances. Volunteered Geographic Information (VGI) has great potential as a valuable data source to decision support systems. Several studies have been proposed to integrate VGI data into DSS. However, as VGI data may have different levels of quality, integrating VGI data with poor quality may affect the decision-making process. In fact, VGI data with poor quality. that are obsolete or incomplete, could, if integrated into a spatial DSS, lead to inappropriate analysis results. This paper presents an approach that aims to enhance spatial DSS analysis and exploitation by integrating high quality VGI data that are appropriate to the user requirements, and that have a good indicator completeness and time relevance. The approach introduces a conceptual framework that evaluates VGI data quality and integrates only high quality VGI data into spatial DSS. The proposed approach is experimented on a road maintenance project in Grand-Tunis. We develop the Map-Report prototype, and we evaluate the efficiency of our approach in enhancing data analysis and exploitation in spatial DSS by reducing the error rate and providing accurate and precise analysis results. Full article

The primary objective of this study was to examine the quality of volunteered geographic information (VGI) data for flood mapping of Hurricane Harvey. As a crowdsourcing platform, the U-Flood project mapped flooded streets in the Houston metro area. This research examines the following: (1) If there are any significant differences in water depth (WD) among the hydraulic and hydrologic (H&H) model, the Federal Emergency Management Agency (FEMA) reference floodplain map, and the VGI? (2) Are there any significant differences in the inundated areas between the floodplain modeled by the VGI and hydraulic simulation? This study used HEC-RAS to simulate flood inundation maps and validated the results with high water marks (HWM) and the FEMA-modeled floodplain after Hurricane Harvey. The statistical results showed that there were significant differences in the WD, the inundated road count, and the length inside/outside of HEC-RAS-modeled floodplain. The results also showed that a less consistent decreasing trend between the U-Flood data and the modeled floodplain over time and space. This study empirically evaluated the data quality of the VGI based on observed and modeled data in flood monitoring. The findings from this study fill the gaps in the literature by assessing the uncertainty and data quality of VGI, providing insights into using supplementary data in flood mapping research. Full article

Background and purpose: Based on artificial intelligence (AI), 3D angiography (3DA) is a novel postprocessing algorithm for “DSA-like” 3D imaging of cerebral vasculature. Because 3DA requires neither mask runs nor digital subtraction as the current standard 3D-DSA does, it has the potential to cut the patient dose by 50%. The object was to evaluate 3DA’s diagnostic value for visualization of intracranial artery stenoses (IAS) compared to 3D-DSA. Materials and methods: 3D-DSA datasets of IAS (n_IAS = 10) were postprocessed using conventional and prototype software (Siemens Healthineers AG, Erlangen, Germany). Matching reconstructions were assessed by two experienced neuroradiologists in consensus reading, considering image quality (IQ), vessel diameters (VD_1/2), vessel-geometry index (VGI = VD₁/VD₂), and specific qualitative/quantitative parameters of IAS (e.g., location, visual IAS grading [low-/medium-/high-grade] and intra-/poststenotic diameters [d_{intra-/poststenotic} in mm]). Using the NASCET criteria, the percentual degree of luminal restriction was calculated. Results: In total, 20 angiographic 3D volumes (n_3DA = 10; n_3D-DSA = 10) were successfully reconstructed with equivalent IQ. Assessment of the vessel geometry in 3DA datasets did not differ significantly from 3D-DSA (VD₁: r = 0.994, p = 0.0001; VD₂:r = 0.994, p = 0.0001; VGI: r = 0.899, p = 0.0001). Qualitative analysis of IAS location (3DA/3D-DSA:n_ICA/C4 = 1, n_ICA/C7 = 1, n_MCA/M1 = 4, n_VA/V4 = 2, n_BA = 2) and the visual IAS grading (3DA/3D-DSA:n_low-grade = 3, n_medium-grade = 5, n_high-grade = 2) revealed identical results for 3DA and 3D-DSA, respectively. Quantitative IAS assessment showed a strong correlation regarding intra-/poststenotic diameters (r_{dintrastenotic} = 0.995, p_{dintrastenotic} = 0.0001; r_{dpoststenotic} = 0.995, p_{dpoststenotic} = 0.0001) and the percentual degree of luminal restriction (r_{NASCET 3DA} = 0.981; p_{NASCET 3DA} = 0.0001). Conclusions: The AI-based 3DA is a resilient algorithm for the visualization of IAS and shows comparable results to 3D-DSA. Hence, 3DA is a promising new method that allows a considerable patient-dose reduction, and its clinical implementation would be highly desirable. Full article

The paper analyses the characteristics of Volunteer Geographic Information (VGI) and the need to assure and assess its quality for a possible use and re-use. Ontologies and soft ontologies are presented as means to support quality assurance and assessment of VGI by highlighting their limitations. A proposal of a possibilistic approach using fuzzy ontology is finally illustrated that allows to model both imprecision and vagueness of domain knowledge and epistemic uncertainty affecting observations. A case study example is illustrated. Full article

Volunteered Geographic Information (VGI) is often collected by non-expert users. This raises concerns about the quality and veracity of such data. There has been much effort to understand and quantify the quality of VGI. Extrinsic measures which compare VGI to authoritative data sources such as National Mapping Agencies are common but the cost and slow update frequency of such data hinder the task. On the other hand, intrinsic measures which compare the data to heuristics or models built from the VGI data are becoming increasingly popular. Supervised machine learning techniques are particularly suitable for intrinsic measures of quality where they can infer and predict the properties of spatial data. In this article we are interested in assessing the quality of semantic information, such as the road type, associated with data in OpenStreetMap (OSM). We have developed a machine learning approach which utilises new intrinsic input features collected from the VGI dataset. Specifically, using our proposed novel approach we obtained an average classification accuracy of 84.12%. This result outperforms existing techniques on the same semantic inference task. The trustworthiness of the data used for developing and training machine learning models is important. To address this issue we have also developed a new measure for this using direct and indirect characteristics of OSM data such as its edit history along with an assessment of the users who contributed the data. An evaluation of the impact of data determined to be trustworthy within the machine learning model shows that the trusted data collected with the new approach improves the prediction accuracy of our machine learning technique. Specifically, our results demonstrate that the classification accuracy of our developed model is 87.75% when applied to a trusted dataset and 57.98% when applied to an untrusted dataset. Consequently, such results can be used to assess the quality of OSM and suggest improvements to the data set. Full article

Volunteered Geographical Information (VGI) and social media can provide information about real-time perceptions, attitudes and behaviours in urban green space (UGS). This paper reviews the use of VGI and social media data in research examining UGS. The current state of the art is described through the analysis of 177 papers to (1) summarise the characteristics and usage of data from different platforms, (2) provide an overview of the research topics using such data sources, and (3) characterise the research approaches based on data pre-processing, data quality assessment and improvement, data analysis and modelling. A number of important limitations and priorities for future research are identified. The limitations include issues of data acquisition and representativeness, data quality, as well as differences across social media platforms in different study areas such as urban and rural areas. The research priorities include a focus on investigating factors related to physical activities in UGS areas, urban park use and accessibility, the use of data from multiple sources and, where appropriate, making more effective use of personal information. In addition, analysis approaches can be extended to examine the network suggested by social media posts that are shared, re-posted or reacted to and by being combined with textual, image and geographical data to extract more representative information for UGS analysis. Full article

Volunteered Geographic Information (VGI) is a widely used data source in various fields and services, such as environmental monitoring, disaster and crisis management, SDI, and mapping. Quality is a critical factor for the usability of VGI. This study focuses on evaluating logical consistency based on the topological relationships between geographic features while considering semantics. It addresses internal (i.e., between thematic layers) and external (i.e., between specific features from different thematic layers) logical consistency. Attribute completeness is computed to support the use of semantics. A tool for assessing the consistency and attribute completeness is designed and implemented in the ArcGIS environment. An open-source web mapping application informs users about VGI consistency with multiscale visualization and indices. Data from OpenStreetMap (OSM), one of the most popular collaborative projects, are evaluated for six European cities: Athens, Berlin, Paris, Utrecht, Vienna, and Zurich. The case study uses OSM-derived data, downloaded from Geofabrik and organized into thematic layers. OSM’s consistency is evaluated and visualized at the regional, city, and feature levels. The results are discussed and conclusions on attribute completeness and consistency are derived. Full article

One of the most important challenges of volunteered geographic information (VGI) is the quality assessment. Existing methods of VGI quality assessment, either assess the quality by comparing a reference map with the VGI map or deriving the quality from the metadata. The first approach does not work for a real-time scenario and the latter delivers approximate values of the quality. Internet of Things (IoT) networks provide real-time observations for environment monitoring. Moreover, they publish more precise information than VGI. This paper introduces a method to assess the quality of VGI in real-time using IoT observations. The proposed method filters sensor observation outliers in the first step. Then it matches sensors and volunteers’ relationships in terms of location, time, and measurement type similarity using a hypergraph model. Then the quality of matched data is assessed by calculating positional and attribute accuracy. To evaluate the method, VGI data of the water level and quality in Tarashk–Bakhtegan–Maharlou water basin is studied. A VGI quality map of the data is assessed by a referenced authoritative map. The output of this step is a VGI quality map, which was used as a reference to check the proposed method quality. Then this reference VGI quality map and the proposed method VGI quality map are compared to assess positional and attribute accuracy. Results demonstrated that 76% of the method results have less than 20 m positional error (i.e., difference with the reference VGI quality map). Additionally, more than 92% of the proposed method VGI data have higher than 90% attribute accuracy in terms of similarity with the reference VGI quality map. These findings support the notion that the proposed method can be used to assess VGI quality in real-time. Full article

Over the past few decades, geoportals have been considered as the key technological solutions for easy access to Earth observation (EO) products, and the implementation of spatial data infrastructure (SDI). However, less attention has been paid to developing an efficient model for crowdsourcing EO products through geoportals. To this end, a new model called the “Open Community-Based Crowdsourcing Geoportal for Earth Observation Products” (OCCGEOP) was proposed in this study. The model was developed based on the concepts of volunteered geographic information (VGI) and community-based geoportals using the latest open technological solutions. The key contribution lies in the conceptualization of the frameworks for automated publishing of standard map services such as the Web Map Service (WMS) and the Web Coverage Service (WCS) from heterogeneous EO products prepared by volunteers as well as the communication portion to request voluntary publication of the map services and giving feedback for quality assessment and assurance. To evaluate the feasibility and performance of the proposed model, a prototype implementation was carried out by conducting a pilot study in Iran. The results showed that the OCCGEOP is compatible with the priorities of the new generations of geoportals, having some unique features and promising performance. Full article

Though Volunteered Geographic Information (VGI) has the advantage of providing free open spatial data, it is prone to vandalism, which may heavily decrease the quality of these data. Therefore, detecting vandalism in VGI may constitute a first way of assessing the data in order to improve their quality. This article explores the ability of supervised machine learning approaches to detect vandalism in OpenStreetMap (OSM) in an automated way. For this purpose, our work includes the construction of a corpus of vandalism data, given that no OSM vandalism corpus is available so far. Then, we investigate the ability of random forest methods to detect vandalism on the created corpus. Experimental results show that random forest classifiers perform well in detecting vandalism in the same geographical regions that were used for training the model and has more issues with vandalism detection in “unfamiliar regions”. Full article

Social media and other forms of volunteered geographic information (VGI) are used frequently as a source of fine-grained big data for research. While employing geographically referenced social media data for a wide array of purposes has become commonplace, the relevant scales over which these data apply to is typically unknown. For researchers to use VGI appropriately (e.g., aggregated to areal units (e.g., neighbourhoods) to elicit key trend or demographic information), general methods for assessing the quality are required, particularly, the explicit linkage of data quality and relevant spatial scales, as there are no accepted standards or sampling controls. We present a data quality metric, the Spatial-comprehensiveness Index (S-COM), which can delineate feasible study areas or spatial extents based on the quality of uneven and dynamic geographically referenced VGI. This scale-sensitive approach to analyzing VGI is demonstrated over different grains with data from two citizen science initiatives. The S-COM index can be used both to assess feasible study extents based on coverage, user-heterogeneity, and density and to find feasible sub-study areas from a larger, indefinite area. The results identified sub-study areas of VGI for focused analysis, allowing for a larger adoption of a similar methodology in multi-scale analyses of VGI. Full article

Volunteered geographic information (VGI) encourages citizens to contribute geographic data voluntarily that helps to enhance geospatial databases. VGI’s significant limitations are trustworthiness and reliability concerning data quality due to the anonymity of data contributors. We propose a data-driven model to address these issues on OpenStreetMap (OSM), a particular case of VGI in recent times. This research examines the hypothesis of evaluating the proficiency of the contributor to assess the credibility of the data contributed. The proposed framework consists of two phases, namely, an exploratory data analysis phase and a learning phase. The former explores OSM data history to perform feature selection, resulting in “OSM Metadata” summarized using principal component analysis. The latter combines unsupervised and supervised learning through K-means for user-clustering and multi-class logistic regression for user classification. We identified five major classes representing user-proficiency levels based on contribution behavior in this study. We tested the framework with India OSM data history, where 17% of users are key contributors, and 27% are unexperienced local users. The results for classifying new users are satisfactory with 95.5% accuracy. Our conclusions recognize the potential of OSM metadata to illustrate the user’s contribution behavior without the knowledge of the user’s profile information. Full article