Remote Sensing for Disaster Science: Building Damage Recognition and Analysis

Dear Colleagues,

Natural disasters such as earthquakes, floods, hurricanes, and wildfires can cause severe damage to the built environment, posing significant challenges for emergency response and post-disaster recovery. The rapid and accurate assessment of building damage is a critical step toward achieving efficient disaster management, optimizing resource allocation, and mitigating secondary risks. With the continuous advancement of remote sensing technology and the widespread availability of satellite imagery, UAV data, LiDAR point clouds, and multimodal sensor data, large-scale, timely disaster damage assessments have gradually become feasible.

At the same time, the rise of deep learning methods has significantly advanced the automatic identification and analysis of building damage based on remote sensing data. However, due to the high complexity of disaster scenarios, significant differences in modalities, resolutions, and imaging mechanisms among multi-source data, as well as the scarcity of high-quality annotated data, existing methods still face issues around insufficient generalization ability and limited stability. Therefore, there is an urgent need to develop building damage assessment methods that can effectively integrate multi-source heterogeneous information, adapt to complex multi-disaster scenarios, and possess robustness, scalability, and interpretability.

This Special Issue aims to bring together the latest research advances in the field of disaster science regarding the identification and assessment of building damage using remote sensing and intelligent analysis. The focus is on integrating multimodal remote sensing data (such as optical imagery, SAR, LiDAR, and UAV data) with advanced machine learning methods (including deep learning and large-scale model technologies) to explore new methods for high-precision, automated, and efficient damage detection and assessment in large-scale scenarios. Additionally, this Special Issue encourages research on scalable computing frameworks and practical deployment capabilities to facilitate the implementation of these technologies in real-world disaster scenarios.

The research scope of this Special Issue aligns closely with the mission of Remote Sensing, particularly in core areas such as Earth observation, geospatial information processing, and remote sensing-based environmental monitoring and disaster management. The Special Issue will prioritize research that combines methodological innovation with practical application, fostering the deep integration and advancement of remote sensing technology and intelligent analysis methods in the field of disaster assessment. 

Topics of interest include, but are not limited to, the following:

• Building damage detection and classification using remote sensing imagery;
• Multi-modal data fusion (e.g., optical imagery, SAR, LiDAR, and UAV data);
• Three-dimensional reconstruction and point cloud analysis for post-disaster assessment;
• Deep learning- and AI-based methods for disaster damage analysis;
• Change detection and time-series analysis in disaster scenarios;
• Rapid mapping and real-time damage assessment systems;
• Benchmark datasets and evaluation protocols for damage recognition;
• Explainable AI and uncertainty modeling in disaster analysis;
• Applications in earthquake, flood, wildfire, and hurricane damage assessment. 

Accepted article types include original research articles, review papers, and methodological studies.

Dr. Fang Fang
Prof. Dr. Shengwen Li
Prof. Dr. Xiao Liu
Dr. Yongyang Xu
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• remote sensing
• disaster damage assessment
• building damage detection
• multi-modal remote sensing
• high-resolution imagery
• SAR-optical fusion
• UAV remote sensing
• LiDAR point clouds
• deep learning
• vision transformers