Special Issue "Advances in Applications of Volunteered Geographic Information"

A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: 31 August 2020.

Special Issue Editors

Dr. Linda See
Website
Guest Editor
International Institute for Applied Systems Analysis (IIASA), Schlossplatz 1, A-2361 Laxenburg, Austria
Interests: citizen science, crowdsourcing and volunteered geographic information (data collection, quality assessment, creating added value products with VGI, motivation and engagement, etc.); land cover/land use validation; creation of hybrid land cover products; serious gaming; sustainable development goals (SDGs)
Special Issues and Collections in MDPI journals
Dr. Cidalia C Fonte
SciProfiles
Guest Editor
Institute for Systems and Computers Engineering at Coimbra, Department of Mathematics, University of Coimbra, 3001-501 Coimbra, Portugal
Interests: spatial data validation and quality assessment; land use land cover mapping; volunteered geographic information; spatial data integration; remote sensing
Special Issues and Collections in MDPI journals
Dr. Marco Minghini

Guest Editor
European Commission-Joint Research Centre (JRC), Via Enrico Fermi 2749, 21027 Ispra (VA), Italy
Interests: crowdsourcing, volunteered geographic information and OpenStreetMap; land cover/land use validation; open source geospatial software; geospatial interoperability; Spatial Data Infrastructures
Special Issues and Collections in MDPI journals
Dr. Vyron Antoniou

Guest Editor
Hellenic Army Geographic Directorate, Mesogeion 227-231, Holargos, 15561, Greece
Interests: GIS; volunteered geographic information; spatial data quality; spatial databases; cartography
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

Since the term volunteered geographic information (VGI) was first coined by Mike Goodchild in 2007, the literature appearing on this topic has grown rapidly. OpenStreetMap (OSM) is one of the most successful examples of VGI, in which volunteers contribute to creating a global map of the world. One of the key characterisics of VGI is the in situ nature of the data. This makes it a new and interesting source of training and validation data for remote sensing, and many papers have already begun to appear on this topic. This Special Issue is intended to highlight the latest advances in VGI for remote sensing, including improvements to classification algorithms, methods by which the data can be used in rigorous validation, and innovative applications of the data. VGI is increasingly being used by national mapping agencies, so papers on this topic are welcome. Finally, VGI has relevance to the Sustainable Development Goals (SDGs) and research on integrating VGI with remote sensing for monitoring the SDG indicators is also of interest.

We would like to invite you to submit articles about your recent research on any of the following topics, where review articles covering one or more of these areas are also very welcome:

  • VGI for training classification algorithms
  • VGI for validation, including considerations of sample design
  • VGI for verification of products from remote sensing
  • VGI related to any thematic area of remote sensing, e.g. land cover, land use, deforestation, urbanization, agriculture, biomass, disaster mapping, etc.
  • VGI and data integration from multiple sources including OSM
  • Issues related to development of VGI campaigns related to remote sensing or Earth observation, e.g. motivation, engagement, sustainability, etc.
  • Novel applications of VGI and remote sensing either web- or mobile-based
  • VGI for change detection and monitoring
  • Applications of VGI use in national mapping agencies
  • VGI for remote sensing applications related to the SDGs.

If there are other topics of relevance to this Special Issue not mentioned above, feel free to contact us to discuss this further.

Dr. Linda See
Dr. Cidália Costa Fonte
Dr. Marco Minghini
Dr. Vyron Antoniou
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 papers will be 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. Remote Sensing is an international peer-reviewed open access semimonthly 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 2200 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

  • Volunteered Geographic Information 
  • Crowdsourcing 
  • Citizen Science 
  • Classification, Data Mining and AI
  • Sample design 
  • Validation 
  • Land Cover/Land Use 
  • Change Detection 
  • SDGs

Published Papers (2 papers)

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Research

Open AccessArticle
Use of Automated Change Detection and VGI Sources for Identifying and Validating Urban Land Use Change
Remote Sens. 2020, 12(7), 1186; https://doi.org/10.3390/rs12071186 - 07 Apr 2020
Cited by 1
Abstract
Land use and land cover (LULC) mapping is often undertaken by national mapping agencies, where these LULC products are used for different types of monitoring and reporting applications. Updating of LULC databases is often done on a multi-year cycle due to the high [...] Read more.
Land use and land cover (LULC) mapping is often undertaken by national mapping agencies, where these LULC products are used for different types of monitoring and reporting applications. Updating of LULC databases is often done on a multi-year cycle due to the high costs involved, so changes are only detected when mapping exercises are repeated. Consequently, the information on LULC can quickly become outdated and hence may be incorrect in some areas. In the current era of big data and Earth observation, change detection algorithms can be used to identify changes in urban areas, which can then be used to automatically update LULC databases on a more continuous basis. However, the change detection algorithm must be validated before the changes can be committed to authoritative databases such as those produced by national mapping agencies. This paper outlines a change detection algorithm for identifying construction sites, which represent ongoing changes in LU, developed in the framework of the LandSense project. We then use volunteered geographic information (VGI) captured through the use of mapathons from a range of different groups of contributors to validate these changes. In total, 105 contributors were involved in the mapathons, producing a total of 2778 observations. The 105 contributors were grouped according to six different user-profiles and were analyzed to understand the impact of the experience of the users on the accuracy assessment. Overall, the results show that the change detection algorithm is able to identify changes in residential land use to an adequate level of accuracy (85%) but changes in infrastructure and industrial sites had lower accuracies (57% and 75 %, respectively), requiring further improvements. In terms of user profiles, the experts in LULC from local authorities, researchers in LULC at the French national mapping agency (IGN), and first-year students with a basic knowledge of geographic information systems had the highest overall accuracies (86.2%, 93.2%, and 85.2%, respectively). Differences in how the users approach the task also emerged, e.g., local authorities used knowledge and context to try to identify types of change while those with no knowledge of LULC (i.e., normal citizens) were quicker to choose ‘Unknown’ when the visual interpretation of a class was more difficult. Full article
(This article belongs to the Special Issue Advances in Applications of Volunteered Geographic Information)
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Open AccessArticle
A Deep Learning Method to Accelerate the Disaster Response Process
Remote Sens. 2020, 12(3), 544; https://doi.org/10.3390/rs12030544 - 06 Feb 2020
Abstract
This paper presents an end-to-end methodology that can be used in the disaster response process. The core element of the proposed method is a deep learning process which enables a helicopter landing site analysis through the identification of soccer fields. The method trains [...] Read more.
This paper presents an end-to-end methodology that can be used in the disaster response process. The core element of the proposed method is a deep learning process which enables a helicopter landing site analysis through the identification of soccer fields. The method trains a deep learning autoencoder with the help of volunteered geographic information and satellite images. The process is mostly automated, it was developed to be applied in a time- and resource-constrained environment and keeps the human factor in the loop in order to control the final decisions. We show that through this process the cognitive load (CL) for an expert image analyst will be reduced by 70%, while the process will successfully identify 85.6% of the potential landing sites. We conclude that the suggested methodology can be used as part of a disaster response process. Full article
(This article belongs to the Special Issue Advances in Applications of Volunteered Geographic Information)
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