Abstract
Road damage assessment holds tremendous potential in evaluating damages and reducing disaster risks to human lives during emergency responses to unforeseen events. The Change Detection (CD) method detects changes in the land surface by comparing bi-temporal remote sensing imageries. Using the CD method for post-disaster assessment, existing research mainly focuses on building, while in terms of road, both the dataset and methodology need to be improved. In response to this, we propose an innovative multi-tasking network that combines Vision Transformer and UNet (BiTransUNet) for identifying road change areas and damage assessments from bi-temporal remote sensing imageries before and after natural disasters, moreover, propose the first road damage assessment model. Notably, our BiTransUNet comprises three efficient modules: Multi-scale Feature Extraction (MFE) module for extracting multi-scale features, Trans and Res Skip Connection (TRSC) module for modeling spatial-temporal global information, and Dense Cased Upsample (DCU) module for change maps reconstruction. In addition, to facilitate our study, we create a new Remote Sensing Road Damage Dataset, RSRDD, thoughtfully designed to contain 1,212 paired imageries before and after disasters, and the corresponding road change masks and road damage levels. Our experimental results on the proposed RSRDD show that our BiTransUNet outperforms current state-of-the-art approaches. BiTransUNet is also applied on the LEVIR-CD building change detection dataset and achieved the best performance, which demonstrates its compatibility in detecting changes of different important ground objects.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Bandara, W.G.C., Patel, V.M.: A transformer-based Siamese network for change detection. In: IGARSS 2022–2022 IEEE International Geoscience and Remote Sensing Symposium, pp. 207–210. IEEE (2022)
Bolorinos, J., Ajami, N.K., Rajagopal, R.: Consumption change detection for urban planning: monitoring and segmenting water customers during drought. Water Resources Res. 56(3), e2019WR025812 (2020)
Cao, K., Zhang, X.: An improved Res-UNet model for tree species classification using airborne high-resolution images. Remote Sens. 12(7), 1128 (2020)
Chen, H., Qi, Z., Shi, Z.: Remote sensing image change detection with transformers. IEEE Trans. Geosci. Remote Sens. 60, 1–14 (2021)
Chen, H., Shi, Z.: A spatial-temporal attention-based method and a new dataset for remote sensing image change detection. Remote Sens. 12(10), 1662 (2020)
Chen, J., et al.: TransuNet: transformers make strong encoders for medical image segmentation. arXiv preprint arXiv:2102.04306 (2021)
Daudt, R.C., Le Saux, B., Boulch, A.: Fully convolutional Siamese networks for change detection. In: 2018 25th IEEE International Conference on Image Processing (ICIP), pp. 4063–4067. IEEE (2018)
Dosovitskiy, A., et al.: An image is worth 16x16 words: transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020)
El-Hattab, M.M.: Applying post classification change detection technique to monitor an Egyptian coastal zone (Abu Qir Bay). Egypt. J. Remote Sens. Space Sci. 19(1), 23–36 (2016)
Fang, S., Li, K., Shao, J., Li, Z.: SNUNet-CD: a densely connected Siamese network for change detection of VHR images. IEEE Geosci. Remote Sens. Lett. 19, 1–5 (2021)
Hamdi, Z.M., Brandmeier, M., Straub, C.: Forest damage assessment using deep learning on high resolution remote sensing data. Remote Sens. 11(17), 1976 (2019)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4700–4708 (2017)
Li, H., Wu, K., Xu, Y.: An integrated change detection method based on spectral unmixing and the CNN for hyperspectral imagery. Remote Sens. 14(11), 2523 (2022)
Li, X., Du, Z., Huang, Y., Tan, Z.: A deep translation (GAN) based change detection network for optical and SAR remote sensing images. ISPRS J. Photogramm. Remote. Sens. 179, 14–34 (2021)
Lin, T.Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017)
López-Fandiño, J., Garea, A.S., Heras, D.B., Argüello, F.: Stacked autoencoders for multiclass change detection in hyperspectral images. In: IGARSS 2018–2018 IEEE International Geoscience and Remote Sensing Symposium, pp. 1906–1909. IEEE (2018)
Milletari, F., Navab, N., Ahmadi, S.A.: V-Net: fully convolutional neural networks for volumetric medical image segmentation. In: 2016 Fourth International Conference on 3D Vision (3DV), pp. 565–571. IEEE (2016)
Nia, K.R., Mori, G.: Building damage assessment using deep learning and ground-level image data. In: 2017 14th Conference on Computer and Robot Vision (CRV), pp. 95–102. IEEE (2017)
Pang, L., Sun, J., Chi, Y., Yang, Y., Zhang, F., Zhang, L.: CD-TransUNet: a hybrid transformer network for the change detection of urban buildings using L-band SAR images. Sustainability 14(16), 9847 (2022)
Papadomanolaki, M., Verma, S., Vakalopoulou, M., Gupta, S., Karantzalos, K.: Detecting urban changes with recurrent neural networks from multitemporal sentinel-2 data. In: IGARSS 2019–2019 IEEE International Geoscience and Remote Sensing Symposium, pp. 214–217. IEEE (2019)
Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28
Tomowski, D., Ehlers, M., Klonus, S.: Colour and texture based change detection for urban disaster analysis. In: 2011 Joint Urban Remote Sensing Event, pp. 329–332. IEEE (2011)
Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, vol. 30 (2017)
Yang, Y., Mehrkanoon, S.: AA-TransUNet: attention augmented TransUNet for nowcasting tasks. In: 2022 International Joint Conference on Neural Networks (IJCNN), pp. 01–08. IEEE (2022)
Yuan, L., et al.: Multi-objects change detection based on Res-Unet. In: 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, pp. 4364–4367. IEEE (2021)
Zhang, Z., Vosselman, G., Gerke, M., Tuia, D., Yang, M.Y.: Change detection between multimodal remote sensing data using Siamese CNN. arXiv preprint arXiv:1807.09562 (2018)
Zheng, Z., Zhong, Y., Wang, J., Ma, A., Zhang, L.: Building damage assessment for rapid disaster response with a deep object-based semantic change detection framework: from natural disasters to man-made disasters. Remote Sens. Environ. 265, 112636 (2021)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Chen, J., Wu, M., Yan, H., Xie, B., Zhang, C. (2024). Change-Aware Network for Damaged Roads Recognition and Assessment Based on Multi-temporal Remote Sensing Imageries. In: Liu, Q., et al. Pattern Recognition and Computer Vision. PRCV 2023. Lecture Notes in Computer Science, vol 14428. Springer, Singapore. https://doi.org/10.1007/978-981-99-8462-6_21
Download citation
DOI: https://doi.org/10.1007/978-981-99-8462-6_21
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-8461-9
Online ISBN: 978-981-99-8462-6
eBook Packages: Computer ScienceComputer Science (R0)