VGTrust: measuring trust for volunteered geographic information

Severinsen, Jeremy; Róiste, Mairéad de; Reitsma, Femke; Hartato, Emir

doi:10.1080/13658816.2019.1572893

Cited by 23 publications

(16 citation statements)

References 28 publications

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“…This editorial highlights how these issues are discussed and addressed by the articles of this special issue and how the papers highlight emerging technologies, concepts, platforms, debates, and methodologies and techniques within VGI and suggest future research directions. This special issue gathered papers on the topics of crowdsourced geospatial data quality (Ballatore and Arsanjani 2018), thematic uncertainty and consistency across data sources (Hervey and Kuhn 2018), spatial biases (Millar et al, 2018), trust issues within VGI (Severinsen et al 2019), and contributors behaviour and interactions (Truong et al 2018).…”

Section: Introductionmentioning

confidence: 99%

Crowdsourced geospatial data quality: challenges and future directions

Basiri

Haklay

Foody

et al. 2019

International Journal of Geographical Information Science

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Section: Introductionmentioning

confidence: 99%

Crowdsourced geospatial data quality: challenges and future directions

Basiri

Haklay

Foody

et al. 2019

International Journal of Geographical Information Science

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“…It has been observed that volunteers' experience and familiarity with the area edited in the map are good proxies to estimate the quality of their annotations in OSM. The authors in [99] propose a measure for estimating annotation trust, by using annotation statistics obtained from volunteers' activities, object geometries, and temporal data. A trust index could be also learned with machine learning methods using OSM data statistics with some reference data.…”

Section: Supporting Users Via Interaction and Skills Estimationmentioning

confidence: 99%

OpenStreetMap: Challenges and Opportunities in Machine Learning and Remote Sensing

Muñoz

Srivastava

Tuia

et al. 2021

IEEE Geosci. Remote Sens. Mag.

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OpenStreetMap (OSM) is a community-based, freely available, editable map service that was created as an alternative to authoritative ones. Given that it is edited mainly by volunteers with different mapping skills, the completeness and quality of its annotations are heterogeneous across different geographical locations. Despite that, OSM has been widely used in several applications in Geosciences, Earth Observation and environmental sciences. In this work, we present a review of recent methods based on machine learning to improve and use OSM data. Such methods aim either 1) at improving the coverage and quality of OSM layers, typically using GIS and remote sensing technologies, or 2) at using the existing OSM layers to train models based on image data to serve applications like navigation or land use classification. We believe that OSM (as well as other sources of open land maps) can change the way we interpret remote sensing data and that the synergy with machine learning can scale participatory map making and its quality to the level needed to serve global and up-to-date land mapping. A preliminary version of this manuscript has been presented in the first authors dissertation [1].

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“…To be as the main datasets, the assessing or usability analysis is necessary and a large body of work proposes using trust as a proxy for VGI quality [35,36]. For example, the research [16] presented a formulaic model that addresses VGI quality issues, by quantifying trust in VGI. Further, a review [37] has classified the related literature into 30 methods that can be used to assess one or more of the 17 quality measures and indicators coming across in the literature for a map, image, and text-based VGI, respectively.…”

Section: Related Workmentioning

confidence: 99%

“…The spatial resolution in this study is set as 50 km, which is considered as the city-level resolution. However, as Tencent positioning big data belongs to Volunteered Geographic Information (VGI) [15], its usability needs to be confirmed because of its diversity of authorship [16]. However, up to now, there is no open-source data source with high spatial-temporal accuracy in the databases covering the whole of China, so we can only consider other similar data for comparative analysis.…”

Section: Introductionmentioning

confidence: 99%

The Prediction of Finely-Grained Spatiotemporal Relative Human Population Density Distributions in China

Zheng

Zhang

2020

IEEE Access

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China's transportation industry has been experiencing huge changes and the travelling frequency of citizens becomes higher and higher and more and more diversified in a short time period. The analysis and deep research on the short-term change of population density in the city-level spatial resolution are worthy of further exploration. In this study, we first used two linear regression models to build relationships between the 2010 census density, predicted 2020 census density and the Tencent density respectively to test the usability of Tencent positioning data. The Pearson's correlation coefficients r 0.58 (p<0.01) and 0.54 (p<0.01) demonstrates good positive correlations between the ground truth (census data) and the geographic spatiotemporal big data (Tencent positioning data), which could be used to represent the relative human population density distribution in China. Then we use the human population distribution based on the Tencent positioning dataset of China in every hour of the first 21 days, to predict the hourly distribution in the next week by seasonal autoregressive-integrated-moving-average (SARIMA) and a convolutional long short-term memory (ConvLSTM) models respectively, with the 50×50 km of spatial resolution. The total average of the ConvLSTM model's Root Mean Square Error (RMSE) is 139.0, while the SARIMA's one is three times greater than the value. And the coefficient of determination (R 2) values of ConvLSTM model is higher than 0.9, while the other ones are about 0.78. Comparing the two results in both time and space concludes that: the evaluation results reflected by RMSE and R 2 showed that the two models are both suitable for the prediction of Tencent density distribution in finely-grained time and space. Nevertheless, the predicted density correlated much better with the tested data at temporal and spatial scales when using ConvLSTM compared to SARIMA, and the capability of prediction in space by ConvLSTM model is more stable. INDEX TERMS Prediction, human population density distribution, SARIMA, ConvLSTM, Tencent positioning data, deep learning, Geographic spatiotemporal big data. Zhi Zheng received a BSc degree in Urban Planning from Peking University, China, in 2016. He is currently pursuing the Ph.D. degree in China academy of science and now a visiting student in National University of Singapore. His research interests include regional development, global production networks, GIS and Remote sensing.

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VGTrust: measuring trust for volunteered geographic information

Cited by 23 publications

References 28 publications

Crowdsourced geospatial data quality: challenges and future directions

Crowdsourced geospatial data quality: challenges and future directions

OpenStreetMap: Challenges and Opportunities in Machine Learning and Remote Sensing

The Prediction of Finely-Grained Spatiotemporal Relative Human Population Density Distributions in China

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