Leveraging Volunteered Geographic Information for Urban and Peri-Urban Area Analysis
A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Urban Remote Sensing".
Deadline for manuscript submissions: closed (31 October 2020) | Viewed by 583
Special Issue Editors
Interests: geographic information systems (GIS); remote sensing; spatial modeling; and data mining for urban and environmental analysis and planning; mapping urban land cover (green space, impervious surfaces, etc.); monitoring forest health using fine resolution satellite imagery
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The quantity of volunteered geographic information (VGI) from OpenStreetMap and other sources is rapidly increasing, and these data have shown great promise in assisting with various remote sensing image analysis tasks (including image classification, data fusion, and accuracy assessment). VGI, a type of geospatial data generated mainly by nonprofessional scientists, represents a growing sub-area of citizen science. VGI data quantity tends to be much higher in (and around) urban areas because there are more potential volunteers living/working there, so it provides a particularly promising source of data for the analysis of urban and peri-urban areas. Despite the great potential offered by volunteered geographic information (mainly owing to the sheer volume of data that can be generated), various challenges exist in using the data for remote sensing analysis, e.g., due to its variable data quality and quantity.
This Special Issue welcomes submissions representing advances in methodologies, strategies, and/or applications for utilizing VGI and remote sensing data for the analysis for urban and/or peri-urban areas. Submissions may cover a wide range of topics including (but not limited to):
- Use of VGI for remote sensing image analysis (e.g., image classification, feature extraction, change detection, accuracy assessment);
- Use of VGI and remote sensing data to elucidate environmental or social problems;
- Investigation of VGI data quality/data quantity issues;
- Methods for analysis of (big-)VGI data.
Dr. Brian Alan Johnson
Prof. Dr. Akio Onishi
Guest Editors
Manuscript Submission Information
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Keywords
- VGI
- Citizen science
- Crowdsourcing
- Data quality issues
- Urban remote sensing
- Big data
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