POTENTIAL OF MULTI-TEMPORAL OBLIQUE AIRBORNE IMAGERY FOR STRUCTURAL DAMAGE ASSESSMENT

7 citations as recorded by crossref.

1. Detection of damaged buildings after an earthquake with convolutional neural networks in conjunction with image segmentation R. Ünlü & R. Kiriş https://doi.org/10.1007/s00371-020-02043-9
2. Post-Disaster Recovery Assessment with Machine Learning-Derived Land Cover and Land Use Information M. Sheykhmousa et al. https://doi.org/10.3390/rs11101174
3. Usability of aerial video footage for 3-D scene reconstruction and structural damage assessment J. Cusicanqui et al. https://doi.org/10.5194/nhess-18-1583-2018
4. Integration and Comparison Methods for Multitemporal Image-Based 2D Annotations in Linked 3D Building Documentation J. Taraben & G. Morgenthal https://doi.org/10.3390/rs14092286
5. Disaster damage detection through synergistic use of deep learning and 3D point cloud features derived from very high resolution oblique aerial images, and multiple-kernel-learning A. Vetrivel et al. https://doi.org/10.1016/j.isprsjprs.2017.03.001
6. Structural Building Damage Detection with Deep Learning: Assessment of a State-of-the-Art CNN in Operational Conditions F. Nex et al. https://doi.org/10.3390/rs11232765
7. Damage Detection and Segmentation in Disaster Environments Using Combined YOLO and Deeplab S. Jo et al. https://doi.org/10.3390/rs16224267