References
- Shen G, Hwang N S, Spatial-Temporal snapshots of global natural disaster impacts Revealed from EM-DAT for 1900-2015 [J] Geomatics, Natural Hazards Risk, 2019, 10(1):912-934.
- Li S, Song W, Fang L, et al. Deep Learning for Hyperspectral Image Classification: An Overview [J]. IEEE Transactions on Geoscience and Remote Sensing, 2019, 57(9):6690-6709.
- Mahdi H, Reza S, Teymoor S S, et al. Earthquake Damage Region Detection by Multitemporal Coherence Map Analysis of Radar and Multispectral Imagery [J]. Remote Sensing, 2021, 13(6):1195-1195.
- Shi D, Ping W, Khushnood A. A survey on deep learning and its applications[J].Computer Science Review, 2021, 40.
- Janiesch C, Zschech P, Heinrich K. Machine learning and deep learning [J]. Electronic Markets, 2021, 31(3):1-11.
- Khan I A, Al-Habsi S. Machine Learning in Computer Vision [J] Procedia Computer Science, 2020, 167(C):1444-1451.
- Mahony O N, Campbell S, Carvalho A, et al. Deep Learning vs Traditional Computer Vision. [J]. CoRR, 2019,abs/1910.13796.
- Wang P, Fan E, Wang P. Comparative Analysis of Image Classification Algorithms Based on Traditional Machine Learning and Deep Learning [J]. Pattern Recognition Letters, 2020.
- Deng J, Jun D, Xiaojing X, et al. A review of research on object detection based on deep learning [J] Journal of Physics: Conference Series, 2020, 1684(1):012028-.
- Shervin M, Y Y B, Fatih P, et al. Image Segmentation Using Deep Learning: A Survey [J] IEEE transactions on pattern analysis and machine intelligence, 2021, PP.
- Ghosh S, Das N, Das I, et al. Understanding Deep Learning Techniques for Image Segmentation [J]. ACM Computing Surveys (CSUR), 2019, 52(4):1-35.
- Wenchao G, Shuang B, Lingxing K. A review on 2D instance segmentation based on deep neural networks [J] Image and Vision Computing, 2022, (prepublish) :104401-.
- S. S M, Biswajeet P.Challenges and limitations of earthquake-induced building damage mapping techniques using remote sensing images-A systematic review [J]. Geocarto International, 2022, 37(21):6186-6212.
- Duarte D, Nex F, Kerle N, et al. Satellite Image Classification of Building Damages Using Airbrone and Satellite Image Samples in a Deep Learning Approach [J] ISPRS Annals of Photogrammetry Remote Sensing & Spatial Information Sciences, 2018, 4(2).
- Xu J Z, Lu W, Li Z, et al. Building damage detection in satellite imagery using convolutional neural networks [J]. arXiv preprint arXiv:1910.06444, 2019.
- Gupta R, Goodman B, Patel N, et al. Creating xBD: A Dataset for Assessing Building Damage from Satellite Imagery[C] Proceedings of the IEEE conference on computer vision and pattern recognition, 2019, 10-17.
- Weber E, Kané H. Building Disaster Damage Assessment in Satellite Imagery with Multi-temporal Fusion [J]. arXiv preprint arXiv:2004.05525, 2020.
- Gupta R, Hosfelt R, Sajeev S, et al. xBD: A Dataset for Assessing Building Damage from Satellite Imagery [J]. arXiv preprint arXiv:1911.09296, 2019.
- Schuegraf P, Bittner K. Automatic Building Footprint Extraction from Multi-Resolution Remote Sensing Images Using a Hybrid FCN [J]. ISPRS International Journal of Geo-Information, 2019, 8(4):191.
- Teja K, Jens L, Felix S, et al. Review on Convolutional Neural Networks (CNN) in vegetation remote sensing [J] ISPRS Journal of Photogrammetry and Remote Sensing, 2021, 17324-49.
- Jia S, Shaohua G, Yunqiang Z, et al. A survey of remote sensing image classification based on CNNs [J]. Big Earth Data, 2019, 3(3):232-254.
- Y. C, X. D, M. L, et al.Dynamic convolution: Attention over convolution kernels [J]. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2020, 11027-11036.
- Andrea A, Francesco D, Francesco I, et al. A survey on modern trainable activation functions [J]. Neural Networks, 2021, 138(prepublish):14-32.
- Afia Z, Muhammad A, Nazri N M, et al. A Comparison of Pooling Methods for Convolutional Neural Networks [J]. Applied Sciences, 2022, 12(17):8643-8643.
- D. C. Siamese Neural Networks: An Overview [J]. Methods in Molecular Biology, 2021, 219073-94.
- Liu X, 0003 Z Y, Zhao J, et al. Siamese Convolutional Neural Networks for Remote Sensing Scene Classification [J]. IEEE Geoscience and Remote Sensing Letters, 2019, 16(8):1200-1204.
- Chen C, Gong W, Chen Y, et al. Object Detection in Remote Sensing Images Based on a Scene-Contextual Feature Pyramid Network [J]. Remote Sensing, 2019, 11(3):339.
- Koonce B, Koonce B. ResNet 50 [J]. Convolutional neural networks with swift for tensorflow: image recognition and dataset categorization, 2021: 63-72.