A Novel 3D Building Damage Detection Method Using Multiple Overlapping UAV Images

20 citations as recorded by crossref.

1. Detecting damaged buildings using a texture feature contribution index from post-earthquake remote sensing images D. Wei & W. Yang 10.1080/2150704X.2019.1692389
2. Detecting Damaged Building Regions Based on Semantic Scene Change from Multi-Temporal High-Resolution Remote Sensing Images J. Tu et al. 10.3390/ijgi6050131
3. Detection of seismic façade damages with multi-temporal oblique aerial imagery D. Duarte et al. 10.1080/15481603.2020.1768768
4. Augmentation Method for anti-vibration hammer on power transimission line based on CycleGAN Y. Tian et al. 10.1080/19479832.2022.2033855
5. Geoscientists in the Sky: Unmanned Aerial Vehicles Responding to Geohazards R. Antoine et al. 10.1007/s10712-020-09611-7
6. Change detection in urban areas from remote sensing data: a multidimensional classification scheme H. Si Salah et al. 10.1080/01431161.2019.1583394
7. Fusion of satellite, aircraft, and UAV data for automatic disaster damage assessment M. Kakooei & Y. Baleghi 10.1080/01431161.2017.1294780
8. Evaluation of effectiveness of three fuzzy systems and three texture extraction methods for building damage detection from post-event LiDAR data M. Janalipour & A. Mohammadzadeh 10.1080/17538947.2017.1387818
9. Identification of Structurally Damaged Areas in Airborne Oblique Images Using a Visual-Bag-of-Words Approach A. Vetrivel et al. 10.3390/rs8030231
10. Detecting building façade damage from oblique aerial images using local symmetry feature and the Gini Index J. Tu et al. 10.1080/2150704X.2017.1312027
11. Deep convolutional neural networks for building extraction from orthoimages and dense image matching point clouds E. Maltezos et al. 10.1117/1.JRS.11.042620
12. Detection of Damaged Rooftop Areas From High-Resolution Aerial Images Based on Visual Bag-of-Words Model J. Tu et al. 10.1109/LGRS.2016.2614298
13. Detecting new building construction in urban areas based on images of small unmanned aerial system H. Ning et al. 10.1080/23754931.2019.1707108
14. Real-Time Damaged Building Region Detection Based on Improved YOLOv5s and Embedded System From UAV Images Y. Wang et al. 10.1109/JSTARS.2023.3268312
15. Community-scale multi-level post-hurricane damage assessment of residential buildings using multi-temporal airborne LiDAR data Z. Zhou et al. 10.1016/j.autcon.2018.10.018
16. Bilevel Thresholding–Based Iterative Analysis for Building-Surface Damage Detection in a Postearthquake Environment M. Areti & Z. Hasnain 10.1061/(ASCE)CP.1943-5487.0001036
17. A review study on unmanned aerial vehicle and mobile robot technologies on damage inspection of reinforced concrete structures J. Wang & T. Ueda 10.1002/suco.202200846
18. Unmanned aerial vehicle-based computer vision for structural vibration measurement and condition assessment: A concise survey K. Zhou et al. 10.1016/j.iintel.2023.100031
19. Identification of Damaged Building Regions From High- Resolution Images Using Superpixel-Based Gradient and Autocorrelation Analysis C. Liu et al. 10.1109/JSTARS.2020.3034378
20. Обґрунтування переваг у використанні оптичних та радарних даних дзз при виявленні будівель, порушених внаслідок природного чи антропогенного впливу Л. Скрипник et al. 10.36023/ujrs.2024.11.4.277