DDNet: disaster damage detection for buildings based on dual-temporal joint attention network

Rapid and accurate assessment of building damage is crucial for effective post-disaster emergency response. The use of pre-disaster and post-disaster satellite imagery is a common approach for detecting building damage. This task involves two essential subtasks: building localization and damage classification. In building localization, the imbalance between buildings and background, along with low recall rates, often leads to boundary deviations, which negatively impact the accuracy of subsequent damage classification. In damage classification, features from both pre-disaster and post-disaster images, combined with localization results, are used; however, variations in imaging modalities and insufficient feature extraction from temporal images can introduce interference and reduce classification performance. To address these challenges, we propose a novel two-stage network, referred to as DDNet. In the first stage, the building localization network utilizes differential upsampling connections to enhance detailed feature acquisition and employs a unified focal loss to mitigate class imbalance between buildings and background, thereby balancing precision and recall. In the second stage, a joint attention module is introduced to effectively mine features from pre-disaster and post-disaster images, leading to improved classification accuracy. Finally, a connected component analysis algorithm is applied to convert pixel-level detection results into building-level damage outputs. On the xBD dataset, the proposed framework achieves a total F1 score of 79.56%, an F1 localization score of 86.38%, and an F1 damage classification score of 76.64%.