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Remote Sensing
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Advances in Applications of Volunteered Geographic Information
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Advances in Applications of Volunteered Geographic Information

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

Deadline for manuscript submissions: closed (31 August 2020) | Viewed by 18219

Special Issue Editors

Dr. Linda See

E-Mail 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, Collections and Topics in MDPI journals
Dr. Cidalia C Fonte

E-Mail Website
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, Collections and Topics in MDPI journals
Dr. Marco Minghini

E-Mail Website
Guest Editor
European Commission-Joint Research Centre (JRC), Via Enrico Fermi 2749, 21027 Ispra, VA, Italy
Interests: volunteered geographic information; OpenStreetMap; landcover/land use validation; open source geospatial software; geospatial interoperability; spatial data infrastructures; data spaces
Special Issues, Collections and Topics in MDPI journals
Dr. Vyron Antoniou

E-Mail
Guest Editor
Hellenic Army Geographic Directorate, Mesogeion 227-231, 15561 Holargos, Greece
Interests: GIS; volunteered geographic information; spatial data quality; spatial databases; cartography
Special Issues, Collections and Topics 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 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. 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 2700 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

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

Published Papers (3 papers)

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Research

31 pages, 7163 KiB  
Article
Automatic Extraction and Filtering of OpenStreetMap Data to Generate Training Datasets for Land Use Land Cover Classification
by Cidália C. Fonte, Joaquim Patriarca, Ismael Jesus and Diogo Duarte
Remote Sens. 2020, 12(20), 3428; https://doi.org/10.3390/rs12203428 - 19 Oct 2020
Cited by 16 | Viewed by 4583
Abstract
This paper tests an automated methodology for generating training data from OpenStreetMap (OSM) to classify Sentinel-2 imagery into Land Use/Land Cover (LULC) classes. Different sets of training data were generated and used as inputs for the image classification. Firstly, OSM data was converted [...] Read more.
This paper tests an automated methodology for generating training data from OpenStreetMap (OSM) to classify Sentinel-2 imagery into Land Use/Land Cover (LULC) classes. Different sets of training data were generated and used as inputs for the image classification. Firstly, OSM data was converted into LULC maps using the OSM2LULC_4T software package. The Random Forest classifier was then trained to classify a time-series of Sentinel-2 imagery into 8 LULC classes with samples extracted from: (1) The LULC maps produced by OSM2LULC_4T (TD0); (2) the TD1 dataset, obtained after removing mixed pixels from TD0; (3) the TD2 dataset, obtained by filtering TD1 using radiometric indices. The classification results were generalized using a majority filter and hybrid maps were created by merging the classification results with the OSM2LULC outputs. The accuracy of all generated maps was assessed using the 2018 official “Carta de Ocupação do Solo” (COS). The methodology was applied to two study areas with different characteristics. The results show that in some cases the filtering procedures improve the training data and the classification results. This automated methodology allowed the production of maps with overall accuracy between 55% and 78% greater than that of COS, even though the used nomenclature includes classes that can be easily confused by the classifiers. Full article
(This article belongs to the Special Issue Advances in Applications of Volunteered Geographic Information)
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22 pages, 8210 KiB  
Article
Use of Automated Change Detection and VGI Sources for Identifying and Validating Urban Land Use Change
by A.-M. Olteanu-Raimond, L. See, M. Schultz, G. Foody, M. Riffler, T. Gasber, L. Jolivet, A. le Bris, Y. Meneroux, L. Liu, M. Poupée and M. Gombert
Remote Sens. 2020, 12(7), 1186; https://doi.org/10.3390/rs12071186 - 7 Apr 2020
Cited by 19 | Viewed by 4918
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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Graphical abstract

19 pages, 5107 KiB  
Article
A Deep Learning Method to Accelerate the Disaster Response Process
by Vyron Antoniou and Chryssy Potsiou
Remote Sens. 2020, 12(3), 544; https://doi.org/10.3390/rs12030544 - 6 Feb 2020
Cited by 20 | Viewed by 6660
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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