Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
6.9
2.8
Journals
IJGI
Special Issues
Crowdsourced Geographic Information in Citizen Science
share announcement

Crowdsourced Geographic Information in Citizen Science

A special issue of ISPRS International Journal of Geo-Information (ISSN 2220-9964).

Deadline for manuscript submissions: closed (30 September 2021) | Viewed by 25996

Special Issue Editors

Prof. Dr. Jean-Claude Thill

E-Mail Website
Guest Editor
Department of Geography and Earth Sciences, University of North Carolina at Charlotte, Charlotte, NC 28223, USA
Interests: spatial analysis; spatial statistics; spatial machine learning; network analysis; computational social science; geospatial big data analytics; urban analytics; spatial-time modeling
Special Issues, Collections and Topics in MDPI journals
Dr. Ran Tao

E-Mail Website
Guest Editor
School of Geosciences, University of South Florida, Tampa, FL 33620, USA
Interests: GIS; spatial statistics; spatial data mining; human mobility; geographic flow
Special Issues, Collections and Topics in MDPI journals
Dr. Zhaoya Gong

E-Mail Website
Guest Editor
School of Geography, Earth and Environmental Sciences, University of Birmingham, Birmingham, UK
Interests: GeoComputation; spatial data science; urban analytics and simulation; urban and regional sciences
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Over the past two decades, citizen science has established itself as a respected way towards evidenced-based knowledge creation across various domains of application. At a time when the value and soundness of scientific research is called into question by prominent political leaders, involvement of citizens as active actors in scientific work is critical to the full transparency of the research process and its broader societal impacts. Volunteer and non-expert individuals can be the “eyes and ears” of professional scientists in locations spanning vast geographic territories, providing first-hand observation of socioeconomic as well as biophysical events that may be unfolding. Crowdsourced spatial information enriches geospatial research in ways unanticipated a short while ago thanks to the emergence of formal and informal social media networks and to the ubiquity of communication and sensing technologies. This Special Issue offers an outlet for papers relevant to crowdsourced spatial information in support of citizen science. We seek papers on theoretical, conceptual, and methodological aspects, including issues of data fusion, data uncertainty and sampling, handling of numerical, graphical, and textual data, technologies of sensing and monitoring, distributed and edge computing, spatial interactions and place semantics, and mobile communication, social networking, ethics, and geoprivacy. We also welcome original applied contributions discussing use cases of crowdsourced spatial information in social and natural science domains. Papers will be reviewed on a continuing basis until the submission deadline.

Prof. Jean-Claude Thill
Dr. Ran Tao
Dr. Zhaoya Gong
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. ISPRS International Journal of Geo-Information is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1900 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Citizen science
  • Volunteered geographic information
  • Location-based social networks
  • Crowdsourcing
  • Citizen sensor
  • Spatiotemporal analysis
  • Data quality
  • Data fusion

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

Published Papers (5 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

25 pages, 24171 KiB  
Article
Using Object Detection on Social Media Images for Urban Bicycle Infrastructure Planning: A Case Study of Dresden
by Martin Knura, Florian Kluger, Moris Zahtila, Jochen Schiewe, Bodo Rosenhahn and Dirk Burghardt
ISPRS Int. J. Geo-Inf. 2021, 10(11), 733; https://doi.org/10.3390/ijgi10110733 - 28 Oct 2021
Cited by 13 | Viewed by 3420
Abstract
With cities reinforcing greener ways of urban mobility, encouraging urban cycling helps to reduce the number of motorized vehicles on the streets. However, that also leads to a significant increase in the number of bicycles in urban areas, making the question of planning [...] Read more.
With cities reinforcing greener ways of urban mobility, encouraging urban cycling helps to reduce the number of motorized vehicles on the streets. However, that also leads to a significant increase in the number of bicycles in urban areas, making the question of planning the cycling infrastructure an important topic. In this paper, we introduce a new method for analyzing the demand for bicycle parking facilities in urban areas based on object detection of social media images. We use a subset of the YFCC100m dataset, a collection of posts from the social media platform Flickr, and utilize a state-of-the-art object detection algorithm to detect and classify moving and parked bicycles in the city of Dresden, Germany. We were able to retrieve the vast majority of bicycles while generating few false positives and classify them as either moving or stationary. We then conducted a case study in which we compare areas with a high density of parked bicycles with the number of currently available parking spots in the same areas and identify potential locations where new bicycle parking facilities can be introduced. With the results of the case study, we show that our approach is a useful additional data source for urban bicycle infrastructure planning because it provides information that is otherwise hard to obtain. Full article
(This article belongs to the Special Issue Crowdsourced Geographic Information in Citizen Science)
►▼ Show Figures

Figure 1

34 pages, 17827 KiB  
Article
An “Animated Spatial Time Machine” in Co-Creation: Reconstructing History Using Gamification Integrated into 3D City Modelling, 4D Web and Transmedia Storytelling
by Mario Matthys, Laure De Cock, John Vermaut, Nico Van de Weghe and Philippe De Maeyer
ISPRS Int. J. Geo-Inf. 2021, 10(7), 460; https://doi.org/10.3390/ijgi10070460 - 6 Jul 2021
Cited by 25 | Viewed by 8815
Abstract
More and more digital 3D city models might evolve into spatiotemporal instruments with time as the 4th dimension. For digitizing the current situation, 3D scanning and photography are suitable tools. The spatial future could be integrated using 3D drawings by public space designers [...] Read more.
More and more digital 3D city models might evolve into spatiotemporal instruments with time as the 4th dimension. For digitizing the current situation, 3D scanning and photography are suitable tools. The spatial future could be integrated using 3D drawings by public space designers and architects. The digital spatial reconstruction of lost historical environments is more complex, expensive and rarely done. Three-dimensional co-creative digital drawing with citizens’ collaboration could be a solution. In 2016, the City of Ghent (Belgium) launched the “3D city game Ghent” project with time as one of the topics, focusing on the reconstruction of disappeared environments. Ghent inhabitants modelled in open-source 3D software and added animated 3D gamification and Transmedia Storytelling, resulting in a 4D web environment and VR/AR/XR applications. This study analyses this low-cost interdisciplinary 3D co-creative process and offers a framework to enable other cities and municipalities to realise a parallel virtual universe (an animated digital twin bringing the past to life). The result of this co-creation is the start of an “Animated Spatial Time Machine” (AniSTMa), a term that was, to the best of our knowledge, never used before. This research ultimately introduces a conceptual 4D space–time diagram with a relation between the current physical situation and a growing number of 3D animated models over time. Full article
(This article belongs to the Special Issue Crowdsourced Geographic Information in Citizen Science)
►▼ Show Figures

Figure 1

18 pages, 3908 KiB  
Article
OSMWatchman: Learning How to Detect Vandalized Contributions in OSM Using a Random Forest Classifier
by Quy Thy Truong, Guillaume Touya and Cyril de Runz
ISPRS Int. J. Geo-Inf. 2020, 9(9), 504; https://doi.org/10.3390/ijgi9090504 - 22 Aug 2020
Cited by 10 | Viewed by 3518
Abstract
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 [...] Read more.
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
(This article belongs to the Special Issue Crowdsourced Geographic Information in Citizen Science)
►▼ Show Figures

Figure 1

10 pages, 2558 KiB  
Communication
Monitoring 2.0: Update on the Halyomorpha halys Invasion of Trentino
by Robert Malek, Livia Zapponi, Anna Eriksson, Marco Ciolli, Valerio Mazzoni, Gianfranco Anfora and Clara Tattoni
ISPRS Int. J. Geo-Inf. 2019, 8(12), 564; https://doi.org/10.3390/ijgi8120564 - 10 Dec 2019
Cited by 5 | Viewed by 3345
Abstract
“BugMap” is a citizen science mobile application that provides a platform for amateur and expert scientists to report sightings of two invasive insect pests, the tiger mosquito Aedes albopictus Skuse (Diptera: Culicidae) and the brown marmorated stink bug, Halyomorpha halys Stål (Hemiptera: Pentatomidae). [...] Read more.
“BugMap” is a citizen science mobile application that provides a platform for amateur and expert scientists to report sightings of two invasive insect pests, the tiger mosquito Aedes albopictus Skuse (Diptera: Culicidae) and the brown marmorated stink bug, Halyomorpha halys Stål (Hemiptera: Pentatomidae). The latter is a notorious pest of fruit trees, vegetables, ornamentals, and row crops, inflicting severe agricultural and ecological disturbances in invaded areas. Our approach consists of coupling traditional monitoring with citizen science to uncover H. halys invasion in Trentino. The project was initiated in 2016 and the first results were reported in 2018. Here, we revisit our initiative four years after its adoption and unravel new information related to the invader dispersal and overwintering capacity. We found that our previous model predicted the current distribution of H. halys in Trentino with an accuracy of 72.5%. A new MaxEnt model was generated by pooling all reports received so far, providing a clearer perspective on areas at risk of stink bug establishment in this north Italian region. The information herein presented is of immediate importance for enhancing monitoring strategies of this pest and for refining its integrated management tactics. Full article
(This article belongs to the Special Issue Crowdsourced Geographic Information in Citizen Science)
►▼ Show Figures

Figure 1

21 pages, 7681 KiB  
Article
Embracing Crowdsensing: An Enhanced Mobile Sensing Solution for Road Anomaly Detection
by Xiao Li, Da Huo, Daniel W. Goldberg, Tianxing Chu, Zhengcong Yin and Tracy Hammond
ISPRS Int. J. Geo-Inf. 2019, 8(9), 412; https://doi.org/10.3390/ijgi8090412 - 13 Sep 2019
Cited by 33 | Viewed by 5571
Abstract
Road anomaly detection is essential in road maintenance and management; however, continuously monitoring road anomalies (such as bumps and potholes) with a low-cost and high-efficiency solution remains a challenging research question. In this study, we put forward an enhanced mobile sensing solution to [...] Read more.
Road anomaly detection is essential in road maintenance and management; however, continuously monitoring road anomalies (such as bumps and potholes) with a low-cost and high-efficiency solution remains a challenging research question. In this study, we put forward an enhanced mobile sensing solution to detect road anomalies using mobile sensed data. We first create a smartphone app to detect irregular vehicle vibrations that usually imply road anomalies. Then, the mobile sensed signals are analyzed through continuous wavelet transform to identify road anomalies and estimate their sizes. Next, we innovatively utilize a spatial clustering method to group multiple driving tests’ results into clusters based on their spatial density patterns. Finally, the optimized detection results are obtained by synthesizing each cluster’s member points. Results demonstrate that our proposed solution can accurately detect road surface anomalies (94.44%) with a high positioning accuracy (within 3.29 meters in average) and an acceptable size estimation error (with a mean error of 14 cm). This study suggests that implementing a crowdsensing solution could substantially improve the effectiveness of traditional road monitoring systems. Full article
(This article belongs to the Special Issue Crowdsourced Geographic Information in Citizen Science)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-5
Back to TopTop