Integrating Machine Learning and Remote Sensing in Disaster Management: A Decadal Review of Post-Disaster Building Damage Assessment
Abstract
:1. Introduction
2. Methodology of the Research
2.1. Article Collection from Sources
- What are the comparative strengths and limitations of remote sensing (satellite, UAV) versus ground-based sensing technologies in the detection and assessment of building damage following various types of disasters (e.g., earthquakes, floods, hurricanes)?
- How can artificial intelligence and deep learning techniques (e.g., CNNs) improve the accuracy and efficiency of building damage assessment from diverse data sources?
- Considering the challenges in real-time data collection and analysis in post-disaster scenarios, what are the most effective AI-driven strategies for rapidly assessing building damage to support immediate response and recovery efforts?
- How do machine learning models compare in their ability to detect, segment, and classify different types of building damage in disaster-affected areas?
2.2. Data Sorting/Article Selection
2.3. Bibliometric Analysis Using Vosviewer
3. Results of Bibliometric Analysis
3.1. Bibliometric Performance Trends
3.1.1. Yearly Publication Metrics
3.1.2. Geographical Distribution of Publications
3.2. Bibliometric Mapping
3.2.1. Co-Occurrence of Keywords
3.2.2. Co-Authorship Network
- Co-Authorship Network of Countries
- Co-Authorship Network of Authors
3.2.3. Citation Analysis
- Citation Analysis by Countries
- Citation Analysis of Sources
- Citation Analysis of Journal Articles
3.2.4. Bibliographic Coupling
4. Discussion
4.1. Techniques for Data Collection in Building Damage Assessment
4.1.1. Satellite-Based Data Collection
4.1.2. UAV-Based Data Collection
4.1.3. Ground-Based Data Collection
4.2. Analytical Techniques for Post-Disaster Building Assessment
4.2.1. Image-Based Analysis
4.2.2. Point Cloud Data Analysis
4.2.3. Radar Remote Sensing Data Analysis
5. Challenges and Future Direction
5.1. Challenges to Be Solved
- Data Quality and Availability
- Integration of Multiple Data Sources
- Processing and Analysis Complexity
5.2. Future Directions
- Advanced-Data Fusion Techniques
- Real-Time Data Processing
- Improving Model Generalization
- Enhancing UAV Capabilities
- Training for the Rescue Team/Disaster Management Team
6. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Al Shafian, S.; Hu, D. Integrating Machine Learning and Remote Sensing in Disaster Management: A Decadal Review of Post-Disaster Building Damage Assessment. Buildings 2024, 14, 2344. https://doi.org/10.3390/buildings14082344
Al Shafian S, Hu D. Integrating Machine Learning and Remote Sensing in Disaster Management: A Decadal Review of Post-Disaster Building Damage Assessment. Buildings. 2024; 14(8):2344. https://doi.org/10.3390/buildings14082344
Chicago/Turabian StyleAl Shafian, Sultan, and Da Hu. 2024. "Integrating Machine Learning and Remote Sensing in Disaster Management: A Decadal Review of Post-Disaster Building Damage Assessment" Buildings 14, no. 8: 2344. https://doi.org/10.3390/buildings14082344