A Review of Advances and Challenges in Intelligent Disaster Assessment of High-Value Objects in Remote Sensing Imagery

ZHANG Yidan; FENG Yingchao; WANG Tianqi; LIU Yu; WANG Mengyu; HOU Zhongyan

doi:10.11999/JEIT251297

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ZHANG Yidan, FENG Yingchao, WANG Tianqi, LIU Yu, WANG Mengyu, HOU Zhongyan. A Review of Advances and Challenges in Intelligent Disaster Assessment of High-Value Objects in Remote Sensing Imagery[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT251297

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ZHANG Yidan, FENG Yingchao, WANG Tianqi, LIU Yu, WANG Mengyu, HOU Zhongyan. A Review of Advances and Challenges in Intelligent Disaster Assessment of High-Value Objects in Remote Sensing Imagery[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT251297

Citation:

ZHANG Yidan, FENG Yingchao, WANG Tianqi, LIU Yu, WANG Mengyu, HOU Zhongyan. A Review of Advances and Challenges in Intelligent Disaster Assessment of High-Value Objects in Remote Sensing Imagery[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT251297

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A Review of Advances and Challenges in Intelligent Disaster Assessment of High-Value Objects in Remote Sensing Imagery

doi: 10.11999/JEIT251297 cstr: 32379.14.JEIT251297

ZHANG Yidan^{1, 2, 3, 4},
FENG Yingchao^{1, 4
,
,},
WANG Tianqi^{1, 2, 3, 4},
LIU Yu^{1, 2, 3, 4},
WANG Mengyu^{1, 2, 3, 4},
HOU Zhongyan^{1, 2, 3, 4}

1.
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China
2.
University of Chinese Academy of Sciences, Beijing 100190, China
3.
(School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China
4.
Key Laboratory of Target Cognition and Application Technology(TCAT), Beijing 100190, China

Funds: National Key R&D Program of China (2024YFF1401001), National Natural Science Foundation of China (62301538), The Science and Disruptive Technology Program, AIRCAS (2025-AIRCAS-SDTP-04)

Accepted Date: 2026-07-02
Rev Recd Date: 2026-07-02

Available Online: 2026-07-12

Abstract

Abstract

Significance The rapid and accurate disaster assessment of objects in the aftermath of disasters is critical for enabling effective emergency response and post-disaster recovery operations. Intelligent remote sensing technologies, empowered by deep learning, provide scalable, objective, and efficient means of assessing disaster across large and complex environments, such as densely populated urban centers, transportation hubs, and critical energy infrastructure. By leveraging high-resolution satellite and aerial imagery, these technologies can provide timely situational awareness for decision-makers, supporting prioritization in rescue and recovery tasks. Despite significant advancements in methods and applications, there remains a lack of comprehensive synthesis in the field, leading to fragmented practices and inconsistent benchmarks across studies. This study addresses this critical gap by providing a structured and systematic review that consolidates technical foundations, commonly used datasets, evaluation metrics, and methodological advances in deep learning-based remote sensing disaster assessment. The overarching goal is to accelerate the adoption and operationalization of these technologies in real-world disaster scenarios, ultimately contributing to the construction of resilient cities and infrastructures under increasing environmental and geopolitical risks. Progress In recent years, deep learning-based remote sensing disaster assessment methods have developed rapidly, demonstrating notable improvements in classification accuracy, processing automation, and scalability. Significant advances include bi-temporal change detection methods that can precisely localize disaster by capturing differences before and after disaster events, and multi-temporal sequence modeling approaches that extract evolving disaster patterns and degradation trends across time-series data. Furthermore, multi-modal data fusion strategies that combine optical, Synthetic Aperture Radar(SAR), and Light Detection and Ranging (LiDAR)data have enhanced the ability to analyze complex disaster characteristics under varying observation conditions. Advanced techniques to address data scarcity, such as transfer learning and self-supervised learning, have further extended the applicability of disaster assessment methods in data-constrained environments. These advances collectively contribute to improving the responsiveness and effectiveness of disaster assessment systems in supporting emergency decision-making and resource allocation during critical disaster events. Conclusions This study systematically categorizes and evaluates the technical landscape of deep learning-based remote sensing disaster assessment for objects, highlighting the strengths and limitations of bi-temporal, multi-temporal, multi-modal, and data-scarce scenario methods. While existing approaches demonstrate considerable potential in addressing various challenges in post-disaster assessment, challenges remain in ensuring robustness across diverse environments and operational conditions. The review underscores the need for standardized disaster classification criteria and comprehensive evaluation frameworks that consider both physical disaster and functional impacts to facilitate practical and consistent disaster assessments in real-world deployments. Prospects Future research in remote sensing-based disaster assessment should focus on developing multi-level collaborative frameworks for evaluating diverse objects across spatial and functional scales, enabling holistic disaster impact assessment that captures both direct and cascading effects. Complex scenarios such as airports, industrial zones, and ports contain static structures, moving objects, and interdependent functional units, thus requiring hierarchical modeling and multi-object reasoning. Physics-driven and hybrid approaches integrating structural mechanics, material degradation, and expert knowledge can further improve interpretability and generalization. Meanwhile, lightweight model design and edge deployment are important for real-time assessment on drones and satellites in emergency situations. Standardized evaluation metrics that combine physical disaster mechanisms with functional impact analysis will also be essential for practical deployment. Together, these directions will help transform intelligent remote sensing technologies into actionable tools for disaster response and recovery.
- Intelligent disaster assessment,
- Remote sensing image,
- Deep Learning

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References(55)

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