Advances and Challenges in Intelligent Damage Assessment of 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. Advances and Challenges in Intelligent Damage Assessment of 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. Advances and Challenges in Intelligent Damage Assessment of Objects in Remote Sensing Imagery[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT251297

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Advances and Challenges in Intelligent Damage Assessment of 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: Huawei NRE Project (OAA22112800424459A), The National Key R&D Program of China (2024YFF1401001), The National Natural Science Foundation of China (62301538), The Science and Disruptive Technology Program, AIRCAS (2025-AIRCAS-SDTP-04)

Received Date: 2025-12-08
Accepted Date: 2026-07-02
Rev Recd Date: 2026-07-01

Available Online: 2026-07-12

Abstract

Abstract

Significance Rapid and accurate damage assessment of high-value objects following disasters is essential for effective emergency response and post-disaster recovery. Deep learning-enabled remote sensing provides a scalable, objective, and efficient approach for assessing disaster damage over large and complex environments, including densely populated urban areas, transportation hubs, and critical infrastructure. By exploiting high-resolution satellite and aerial imagery, these methods provide timely situational awareness to support rescue prioritization and recovery planning. Despite substantial advances in algorithms and applications, the field still lacks a comprehensive review, leading to fragmented technical development and inconsistent evaluation practices. This paper systematically reviews the technical foundations of intelligent damage assessment in remote sensing, including damage classification standards, publicly available datasets, evaluation metrics, and representative deep learning methods. The review aims to facilitate the practical deployment of intelligent remote sensing technologies for disaster response under increasing natural and human-induced hazards. Progress Deep learning-based damage assessment methods for remote sensing imagery have advanced rapidly, with substantial improvements in assessment accuracy, automation, and scalability. Representative developments include Bi-temporal Change Detection methods, which identify damage by comparing pre-disaster and post-disaster imagery, and Multi-temporal Sequence Modeling methods, which characterize the temporal evolution of damage using image sequences. Multi-modal Data Fusion methods that integrate optical imagery, Synthetic Aperture Radar (SAR), and Light Detection And Ranging (LiDAR) data further improve damage assessment under complex imaging conditions by exploiting complementary information from multiple sensors. In addition, methods designed for data-constrained scenarios, including transfer learning, semi-supervised learning, self-supervised learning, and domain adaptation, improve model robustness and generalization when labeled data are limited. These advances substantially improve the efficiency, reliability, and applicability of intelligent damage assessment systems for emergency response and resource allocation. Conclusions This paper systematically summarizes the technical landscape of deep learning-based damage assessment of high-value objects in remote sensing imagery. Existing methods are categorized into four major groups: Bi-temporal Change Detection, Multi-temporal Sequence Modeling, Multi-modal Data Fusion, and methods for Data-Constrained Scenarios. Their technical characteristics, strengths, and limitations are systematically analyzed and compared. Although these methods have demonstrated promising performance in post-disaster damage assessment, several challenges remain, including limited robustness across diverse environments, insufficient exploitation of temporal and multimodal information, and inadequate generalization under limited training data. In addition, unified damage classification standards and comprehensive evaluation frameworks remain unavailable, limiting the consistency, comparability, and practical applicability of current assessment systems. Prospects Future research should focus on developing hierarchical collaborative frameworks for damage assessment across multiple object types, spatial scales, and functional levels to characterize both direct physical damage and cascading functional degradation. Complex environments, including airports, industrial facilities, and ports, contain static infrastructure, moving objects, and highly interconnected functional units, requiring hierarchical scene understanding and object-level reasoning. Physics-informed and hybrid learning frameworks that integrate structural mechanics, material degradation mechanisms, and domain knowledge are expected to improve model interpretability and generalization. Furthermore, lightweight model architectures and edge deployment strategies will be essential for real-time damage assessment on unmanned aerial vehicles and satellite platforms. Standardized evaluation systems that jointly consider physical damage and functional degradation will further facilitate practical deployment in emergency response and post-disaster recovery.
- Intelligent disaster assessment,
- Remote sensing image,
- Deep Learning (DL)

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References(54)

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