Multimodal Data Fusion for Urban Environmental Monitoring and Management
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Radar Sensors".
Deadline for manuscript submissions: closed (20 June 2022) | Viewed by 9268
Special Issue Editors
Interests: object-based image analysis (GEOBIA); artificial intelligence (e.g., deep learning) for remote sensing image interpretation; geospatial data mining and understanding; remote sensing of urban environment
Interests: remote sensing; forest disturbances; GEOBIA; spatial ecology
Special Issues, Collections and Topics in MDPI journals
Interests: image classification; spatial analysis; deep learning; sample learning; urban landscape
Special Issues, Collections and Topics in MDPI journals
Interests: urban remote sensing; ecological remote sensing; GIS; extensive data analysis; natural resource remote sensing monitoring and assessment
Special Issues, Collections and Topics in MDPI journals
2. Center for Geospatial Analysis, College of William and Mary, Williamsburg, VA 23185, USA
Interests: geospatial analysis of land and vegetation dynamics; vegetation (forest and crop) phenology; invasive plant light detection and ranging; drones
Special Issue Information
Dear Colleagues,
The causes and pressures of the majority of today's environmental problems can be traced back, directly or indirectly, to urban areas. The forces and processes that constitute "urban activity" have far-reaching and long-term effects on its immediate boundaries and the entire region in which it is positioned. Over recent decades, Earth Observation (E.O.) for urban areas has become an essential means of characterizing urban sprawl, monitoring the consequences of anthropogenic activities within cities, and offering critical findings to assist urban researchers or managers in making informed decisions. With enhanced E.O. capabilities, remote sensing data from multiple platforms, multiple sensors, and multiple dates are becoming ubiquitous. Advanced physical and machine learning models have facilitated multimodal data integration for urban environmental studies with promising results. However, the unification of information for urban applications remains challenging due to (i) finding appropriate information fusion strategies that can adapt to various urban environments being difficult and (ii) balancing big data analytics and information needs for dealing with specific environmental issues is tricky.
This Special Issue invites submissions on the latest advances in multimodal data fusion for mapping and monitoring the urban environment. The focus of the contributions to the Special Issue will be on reviewing current progress, highlighting the latest methodologies proposed to respond to the needs of multimodal data processing for urban environmental monitoring and management, and pointing out the strategies that may meet the requirements of potential applications.
The topics of interest include (but not limited to):
- Novel, computationally efficient algorithms for the processing and fusion of data from multi-sensors, multi-sources, and multi-temporal acquisitions;
- New methodologies, e.g., data registration, efficient processing for complex, big data, data quality assurance/pre-processing, deep learning, etc., for urban environment monitoring and management;
- Innovative applications for urban change detection, LCLU mapping, disaster monitoring, responses, etc.;
- Interdisciplinary and higher-level studies on various aspects of employing multimodal data fusion such as feasibility, strength, challenges, and effectiveness.
Dr. Yindan Zhang
Dr. Gang Chen
Prof. Dr. Shihong Du
Prof. Dr. Zhifeng Wu
Dr. Kunwar K. Singh
Guest Editors
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Keywords
- multimodal
- data fusion
- urban remote sensing
- land-cove and land-use (LCLU) mapping
- change detection
- disaster monitoring and responses
- data assurance/pre-processing
- deep learning
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