Keys to successful scientific VGI projects

Ingensand, Jens; Composto, Sarah; Ertz, Olivier; Rappo, Daniel; Nappez, Marion; Produit, Timothée; Oberson, Mathias; Widmer, Ivo; Joost, Stéphane

doi:10.7287/peerj.preprints.2230v1

Cited by 2 publications

(2 citation statements)

References 1 publication

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“…In order to make crowdsourcing project work, it faces many challenges, range from get users participation to keep user participation, from control data quality to decide on the legal aspects [19]. Literature [20] emphases the importance of motivations.…”

Section: Combination Of Algorithms and Crowdsourcingmentioning

confidence: 99%

Building a Crowdsourcing based Disabled Pedestrian Level of Service routing application using Computer Vision and Machine Learning

Blanc

Liu

Ertz

et al. 2019

2019 16th IEEE Annual Consumer Communications &Amp; Networking Conference (CCNC)

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The availability of global and scalable tools to assess disabled pedestrian level of service (DPLoS) is a real need, yet still a challenge in today's world. This is due to the lack of tools that can ease the measurement of a level of service adapted to disabled people, and also to the limitation concerns about the availability of information regarding the existing level of service, especially in real time. This paper describes preliminary results to progress on those needs. It also includes a design for a navigation tool that can help a disabled person move around a city by suggesting the most adapted routes according to the person's disabilities. The main topics are how to use advanced computer vision technologies, and how to benefit from the prevalence of handheld devices. Our approach intends to show how crowdsourcing techniques can improve data quality by gathering and combining up-to-date data with valuable field observations.

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Section: Combination Of Algorithms and Crowdsourcingmentioning

confidence: 99%

Building a Crowdsourcing based Disabled Pedestrian Level of Service routing application using Computer Vision and Machine Learning

Blanc

Liu

Ertz

et al. 2019

2019 16th IEEE Annual Consumer Communications &Amp; Networking Conference (CCNC)

Self Cite

View full text Add to dashboard Cite

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“…Benefiting from numerous volunteers and their familiarity with surrounding features, geospatial data are updated on the OSM platform frequently and quickly. Now various applications are derived and expanded on OSM spatial data because the data is free for all uses [2]. And OSM is a potential source of geospatial open data for monitoring sustainable development goals (SDG) indicators [3].…”

Section: Introductionmentioning

confidence: 99%

MSC-DeepFM: OSM Road Type Prediction via Integrating Spatial Context Using DeepFM

Zhao,

Ning,

et al. 2023

Sustainability

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The quality of OpenStreetMap (OSM) has been widely concerned as a valuable source for monitoring some sustainable development goals (SDG) indicators. Improving its semantic quality is still challenging. As a kind of solution, road type prediction plays an important role. However, most existing algorithms show low accuracy, owing to data sparseness and inaccurate description. To address these problems, we propose a novel OSM road type prediction approach via integrating multiple spatial contexts with DeepFM, named MSC-DeepFM. A deep learning model DeepFM is used for dealing with data sparseness. Moreover, multiple spatial contexts (MSC), including the features of intersecting roads, surrounding buildings, and points of interest (POIs), are distilled to describe multiple types of road more accurately. The MSC combined with geometric features and restricted features are put into DeepFM, in which the low-order and high-order features fully interact. And a multivariate classifier OneVsRest is adopted to predict road types. Experiments on OSM show that the proposed model MSC-DeepFM achieves excellent performance and outperforms some state-of-the-art methods.

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Keys to successful scientific VGI projects

Cited by 2 publications

References 1 publication

Building a Crowdsourcing based Disabled Pedestrian Level of Service routing application using Computer Vision and Machine Learning

Building a Crowdsourcing based Disabled Pedestrian Level of Service routing application using Computer Vision and Machine Learning

MSC-DeepFM: OSM Road Type Prediction via Integrating Spatial Context Using DeepFM

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