Author:
Xia Zaishuo,Li Zelin,Bai Yanbing,Yu Jinze,Adriano Bruno
Publisher
Springer International Publishing
Reference24 articles.
1. Rudner, T.G., et al.: Multi3Net: segmenting flooded buildings via fusion of multiresolution, multisensor, and multitemporal satellite imagery. Proc. AAAI Conf. Artif. Intell. 33(01), 702–709 (2019)
2. Kyrkou, C., Theocharides, T.: EmergencyNet: efficient aerial image classification for drone-based emergency monitoring using atrous convolutional feature fusion. IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens. 13, 1687–1699 (2020)
3. 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)
4. Gupta, R., Shah, M.: RescueNet: joint building segmentation and damage assessment from satellite imagery. In: 2020 25th International Conference on Pattern Recognition (ICPR). IEEE, pp. 4405–4411 (2021)
5. Adriano, B., et al.: Learning from multimodal and multitemporal earth observation data for building damage mapping. ISPRS J. Photogramm. Remote. Sens. 175, 132–143 (2021)
Cited by
6 articles.
订阅此论文施引文献
订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献