Urban Environments Modeling using Very-High-Resolution Imagery and Crowdsourced Geospatial Data
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 2019) | Viewed by 4667
Special Issue Editors
Interests: image classification; spatial analysis; deep learning; sample learning; urban landscape
Special Issues, Collections and Topics in MDPI journals
Interests: urban remote sensing; spatial analysis; spectral analysis; land use land cover mapping
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Very high resolution (VHR) remote sensing imagery and crowdsourced geospatial data provide innovative means for monitoring and modeling urban environments. The launches of commercial satellites with very high spatial resolution (VHR) sensors (e.g. IKONOS, QuickBird, Worldview and Gaofen), as well as unmanned aerial vehicles (VAVs) with VHR aerial photos and LiDAR data, bring a nonparallel opportunity for analyzing physical elements in urban environments. Moreover, crowdsourced geospatial data (e.g., OpenStreetMap, Point of Interest, and social media) bring new approaches to observe human-related characteristics of urban environments. Contrary to the availability of VHR (e.g. spatial, spectral, temporal, angle) imagery and crowdsourced geospatial data, the developments of state-of-the-art analytical techniques and novel applications in urban environments are still limited. It is highly necessary to develop innovative technologies and applications for creating a sustainable urban environment and alleviating negative impacts of urbanization.
This special issue calls for innovative techniques and novel applications for analyzing urban environments using VHR remote sensing imagery and crowdsourced geospatial data. Topics include but not limited to:
- urban elements classification and information extraction from multi-source spatial data,
- fusion and integration of VHR images and crowdsourced geospatial data,
- urban change detection and dynamic analysis,
- machine learning and spatiotemporal statistic methods,
- image and data mining from multi-source, multi-scale, multi-temporal data,
- urban growth and land use patterns and changes
- urban population, energy consuming, impervious surfaces modeling
- urban heat island and urban pollution
- urban air, water and green space and their dynamics,
- biodiversity loss and degradation,
- human-environment interactions in urban environments, and
- measuring indicators of sustainable urban development.
Prof. Shihong Du
Prof. Changshan Wu
Guest Editors
Manuscript Submission Information
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Keywords
- Very-High-Resolution remote sensing
- Crowdsourced geospatial data
- Geographic object-based image analysis
- Image and data mining
- Spatial data analysis
- Urban Environments
- Land cover and land use
- Human-environment interactions
- Sustainable urban development
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