Technological Advancements in Disaster Damage Assessment Using Earth Observation, Machine Learning, and Numerical Simulation
A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Environmental Remote Sensing".
Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 3508
Special Issue Editors
Interests: remote sensing; machine learning; numerical simulation; disaster science
Special Issues, Collections and Topics in MDPI journals
Interests: multi-agent systems and agent-based simulation; tsunami simulation; evacuation simulation; remote sensing
Special Issues, Collections and Topics in MDPI journals
Interests: remote sensing; machine learning; intelligent evacuation systems; natural hazards; disaster management; earthquake engineering; informal urban growth
Interests: earthquake engineering; geospatial analysis for damage assessment; remote sensing for disaster response; DEM analysis for geomorphology
Special Issues, Collections and Topics in MDPI journals
Interests: remote sensing; geographic information systems; digital image processing; catastral information; damage assessment; internet of things; machine learning; infrastructure risk assessment
Special Issue Information
Dear Colleagues,
The combination of remote sensing, physics-based numerical simulation, and advanced machine learning technologies is instrumental in understanding several aspects of the Earth's ever-changing surface. Mainly, disaster science research has seen considerable attention, yielding novel methodologies for rapid post-disaster damage assessments and an accurate understanding of hazard scenarios before disasters. On the one hand, integrating numerical modeling and machine learning technologies allows us to analyze several Earth phenomena, such as ground deformations, growing urban environments, and local-site characterization. Furthermore, earth observation technologies, such as optical imaging and radar-based sensing, can capture changes before and after disasters. However, complex and unique disaster conditions induced by earthquakes, heavy rain, and other natural phenomena present significant challenges due to several factors, such as data accessibility, missing information, and high computation cost. Thus, this Special Issue explores the theory and combined application of numerical modeling, machine learning, and remote sensing technologies for disaster damage and loss assessments.
This Special Issue is open to all contributions on recent advances and novel developments of methodologies and best-case study application of computer simulation, remote sensing, and machine to earthquakes, tsunamis, volcanic, and flooding events. We encourage submissions of both review and original research articles related, but not limited, to the following topics:
- Analysis of changes in urban environment;
- Damage recognition and mapping;
- Machine learning for disaster research;
- Detection and classification of building damage;
- Extraction and mapping of flooded areas;
- Time-series analysis of surface deformations;
- Open data and big data for multi-hazard analysis;
- Natural hazard modeling and prediction.
Dr. Bruno Adriano
Dr. Erick Mas
Dr. Luis Angel Moya Huallpa
Dr. Hiroyuki Miura
Prof. Dr. Miguel Estrada
Guest Editors
Manuscript Submission Information
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Keywords
- earthquake damage
- tsunami events
- flood damage
- loss estimation
- optical imaging
- synthetic aperture radar
- machine learning
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