A Deep Learning Framework for Rapid Building Damage Detection through Multimodal Data Fusion: Application to the 2025 Myanmar Earthquake

Framework for Cross-Disaster Building Damage Assessment Using Cost-Sensitive Learning

Rapid and reliable assessment of structural damage following disasters is critical for prioritizing rescue operations. In this study, we present a unified deep learning framework for building damage assessment from satellite imagery. The proposed approach integrates segmentation-driven feature construction, morphological processing, cost-sensitive learning, and cross-disaster evaluation to support robust performance under limited and imbalanced data conditions. The framework combines an adapted U-Net for building localization with a hybrid convolutional neural network (CNN)-deep neural network (DNN) classifier for damage-level prediction and evaluates transferability across disaster events, geographic regions, and sensing conditions. The proposed method is evaluated on selected events from the xView2 Building Damage Assessment (xBD) and BRIGHT datasets, using optical imagery from xBD and pre-disaster optical and post-disaster Synthetic Aperture Radar (SAR) imagery from BRIGHT. Despite the limited and highly imbalanced event-specific samples, the framework achieves a mean cross-validation macro-F1 score of 70% and a maximum fold-level score of 77% on the Mexico earthquake subset of xBD and up to 98% on earthquake-related events in BRIGHT. Cross-validation characterizes performance variability across source-image-grouped data partitions, while cross-disaster evaluation reveals event-dependent transferability and provides a preliminary indication that structural domain similarity may be related to transfer performance. Although the evaluation is constrained by data availability, the results indicate that lightweight, cost-sensitive deep learning frameworks may support auxiliary post-disaster screening and decision support in resource-constrained scenarios. This study highlights both the potential and the remaining challenges of deploying artificial intelligence (AI) for rapid post-disaster assessment.

Rapid post-earthquake damage assessment using patch-level CNNs and VLM

This study explores AI-driven image classification to expedite damage evaluation by identifying damaged buildings from post-disaster photos much faster than conventional methods, providing a more detailed understanding of structural integrity across affected areas.

Rapid Building Damage Detection from Remote Sensing Images: a Novel Lightweight Network with Contrastive Learning

A novel lightweight Local-Global Interaction Network (LGINet) for efficient BDD, which achieves the best balance between accuracy and efficiency, outperforming existing methods.

Assessing the Benefits of Combining Advanced Deep Learning Techniques for Post-Disaster Building Damage Assessment from UAV Imagery

A hybrid framework that decouples detection from damage assessment is proposed, combining the precision of CV models with the reasoning power of LVLMs, and the best combination under this framework accurately counts intact, partially damaged and completely destroyed buildings.

Landslide identification method based on enhanced DeepLabv3+ architecture: joint optimization of bottleneck attention and dual-module graph convolution

CalFireSegNet: a lightweight hybrid attention–transformer network for post-wildfire building footprint change analysis using high-resolution satellite imagery

Natural disasters, particularly wildfires, cause severe human, environmental, and economic losses worldwide. Rapid and accurate identification of building footprints and potential potential structural changes is essential for effective emergency response, search-and-rescue operations, and post-disaster recovery planning. To address the challenges of identifying building loss from remote sensing imagery, this study proposes CalFireSegNet, a lightweight hybrid attention–transformer network for post-wildfire building footprint extraction and loss proxy detection from satellite imagery. The proposed architecture integrates depthwise convolutions, convolutional block attention modules (CBAM), atrous spatial pyramid pooling (ASPP), and Transformer blocks to effectively capture both local structural details and long-range contextual dependencies while maintaining low computational complexity. The model was trained and evaluated using benchmark building segmentation datasets (Inria and WHU) and subsequently applied to pre- and post-event satellite imagery from the recent California wildfires. Experimental results demonstrate that CalFireSegNet achieves superior performance compared with several state-of-the-art semantic segmentation models, including U-Net, PSPNet, DeepLabv3+, ENet, HRNet, and SegNet, obtaining 98.45% accuracy, 94.35% mIoU, and 95.03% Dice Similarity Score while requiring only 3.72 million parameters. Furthermore, a lightweight mask-difference framework was developed to generate a spatial proxy of potential building footprint loss using pre- and post-event satellite pairs. Since publicly available building-level damage annotations for recent California wildfire events remain limited, the real-world wildfire experiments are presented as a validation of cross-domain applicability rather than a fully supervised structural loss proxy estimation benchmark.