References
- United Nations Office for Disaster Risk Reduction (UNDRR). (2021). Human cost of disasters: Overview of the last 20 years (2000-2019). Retrieved from https://www.undrr.org/publication/human-cost-disastersoverview-last-20-years-2000-2019
- Centre for Research on the Epidemiology of Disasters (CRED). (2023). 2023 Disasters in Numbers: A Significant Year of Disaster Impact. Retrieved from https://reliefweb.int/report/world/2023-disasters-numbers
- 김태훈, 윤준희(한국건설기술연구원 미래융합연구본부). 대형복합재난의 효율적 관리를 위한 제도개선방안 연구. 한국산학기술학회논문지 = Journal of the Korea Academia-Industrial cooperation Society, v.19 no.5, 2018 년.
- 황승호, 김계현, 이미란. (2015-09-17). 재해지역 피해조사를 위한 중.저해상도 위성영상을 활용한 피해탐지 프로세스 구축에 관한 연구. 대한공간정보학회 학술대회, 서울.
- Deng, Liwei, and Yue Wang. "Post-disaster building damage assessment based on improved U-Net." Scientific reports 12.1 (2022): 15862.
- Garcia-Garcia, Alberto, et al. "A review on deep learning techniques applied to semantic segmentation." arXiv preprint arXiv:1704.06857 (2017).
- W. Wang, N. Yang, Y. Zhang, F. Wang, T. Cao, and P. Eklund, "A review of road extraction from remote sensing images," Journal of Traffic and Transportation Engineering (English Edition), vol. 3, no. 3, pp. 271-282, 2016.
- S. Tian, X. Zhang, J. Tian, and Q. Sun, "Random forest classification of wetland landcovers from multi-sensor data in the arid region of Xinjiang, China," Remote Sensing, vol. 8, no. 11, p. 954, 2016.
- R. Girshick, J. Donahue, T. Darrell, and J. Malik, "Rich feature hierarchies for accurate object detection and semantic segmentation," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2014, pp. 580-587.
- DeepGlobe. (n.d.). DeepGlobe 2018: Satellite challenge. DeepGlobe. http://deepglobe.org/
- Xview2. (n.d.). Xview2: Building damage assessment challenge. Xview2. https://xview2.org/
- Mnih, V. (2013). Machine learning for aerial image labeling [Doctoral dissertation, University of Toronto]. University of Toronto. https://www.cs.toronto.edu/~vmnih/data/