Vector-Guided Post-Earthquake Damaged Road Extraction Using Diffusion-Augmented Remote Sensing Imagery.
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| Title: | Vector-Guided Post-Earthquake Damaged Road Extraction Using Diffusion-Augmented Remote Sensing Imagery. |
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| Authors: | Qu, Chenyao1 (AUTHOR), Jiang, Jinxiang1,2 (AUTHOR), Wu, Zhimin1,3 (AUTHOR), Hassan, Talha1 (AUTHOR), Wang, Wei1,2 (AUTHOR) wangweicn@csu.edu.cn, Miao, Zelang1,3 (AUTHOR), Tang, Hong2 (AUTHOR), Liu, Kun3 (AUTHOR), Wu, Lixin1 (AUTHOR) |
| Source: | Remote Sensing. Feb2026, Vol. 18 Issue 4, p613. 21p. |
| Subjects: | Stable Diffusion, Synthetic data, Remote sensing, Image segmentation, Earthquake damage, Emergency management |
| Geographic Terms: | Turkey |
| Abstract: | Highlights: What are the main findings? An automated data simulation strategy based on stable diffusion model and topological constraints was constructed, generating high-fidelity synthetic datasets to effectively mitigate the extreme scarcity of post-earthquake labeled samples. A vector-guided segmentation model (VRD-U2Net) based on diffusion-generated synthetic training data was developed, achieving a mIoU of 0.884 on synthetic datasets and a zero-shot F1-score of 65.3% on real-world Turkey earthquake imagery. What are the implications of the main findings? The study validates that diffusion-based generative models serve as a reliable data source for training disaster assessment algorithms, offering a scalable solution to the persistent bottleneck of labeled data scarcity in emergency response. Integrating vector priors provides robust geometric guidance that is essential for maintaining the topological integrity of linear features in complex post-disaster scenes, suggesting that multi-modal fusion is critical for reliable automated damage detection. Destructive earthquakes frequently sever transportation lifelines, significantly impeding the progress of emergency rescue and post-disaster reconstruction efforts. The automated identification of road damage utilizing high-resolution remote sensing imagery is strictly constrained by the scarcity of post-disaster labeled samples and the morphological complexity of road networks. Consequently, model segmentation results frequently suffer from discontinuities in topological connectivity and confusion between background features and damaged roads. To address these challenges, this study proposes a road damage detection framework that integrates generative artificial intelligence with vector prior knowledge. A data simulation pipeline utilizing a stable diffusion model was constructed, employing topologically constrained masking to generate high-fidelity synthetic damage samples based on the DeepGlobe dataset, thereby mitigating the data deficit. The proposed Vector-Guided Damaged Road Segmentation Network (VRD-U2Net) employs wavelet convolutions (WTConv) to decouple high-frequency noise from low-frequency structural components and utilizes a Multi-Scale Residual Attention (MSRA) module to align visual features with vector priors. Furthermore, a vector-prior-driven dynamic upsampling mechanism is introduced to enforce geometric constraints on model predictions. Experimental results demonstrate that the method achieves an mIoU of 0.884 on the synthetic dataset. In validation using real-world imagery from the 2023 Turkey earthquake, the model attained an F1-score of 65.3% and recall of 72.3% without fine-tuning, exhibiting robust generalization capabilities to support manual damage assessment in data-scarce emergency scenarios. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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