AERIAL IMAGES AND LIDAR DATA FUSION FOR DISASTER CHANGE DETECTION

14 citations as recorded by crossref.

1. LiDAR-guided dense matching for detecting changes and updating of buildings in Airborne LiDAR data K. Zhou et al.
2. Identification of Damaged Building Regions From High- Resolution Images Using Superpixel-Based Gradient and Autocorrelation Analysis C. Liu et al.
3. Building Change Detection Using a Shape Context Similarity Model for LiDAR Data X. Lyu et al.
4. Automated detection of damaged buildings in post-disaster scenarios: a case study of Kahramanmaraş (Türkiye) earthquakes on February 6, 2023 C. Serifoglu Yilmaz et al.
5. Classification of lidar bare-earth points, buildings, vegetation, and small objects based on region growing and angular classifier J. Sánchez-Lopera & J. Lerma
6. Effective Generation and Update of a Building Map Database Through Automatic Building Change Detection from LiDAR Point Cloud Data M. Awrangjeb
7. Identification of damage in buildings based on gaps in 3D point clouds from very high resolution oblique airborne images A. Vetrivel et al.
8. Entropy-Based Fusion of Water Indices and DSM Derivatives for Automatic Water Surfaces Extraction and Flood Monitoring P. Horkaew & S. Puttinaovarat
9. Concatenated Deep-Learning Framework for Multitask Change Detection of Optical and SAR Images Z. Du et al.
10. Tsunami damage assessment and monitoring of reconstruction in Nagapattinam area using multispectral images G. Jiji & G. Merlin
11. Building Damage Detection of the 2004 Nagapattinam, India, Tsunami Using the Texture and Spectral Features from IKONOS Images G. Merlin & G. Jiji
12. BDD-Net: An End-to-End Multiscale Residual CNN for Earthquake-Induced Building Damage Detection S. Seydi et al.
13. An approach to urban building height and floor estimation by using LiDAR data A. Erener et al.
14. HiCD: Change Detection in Quality-Varied Images via Hierarchical Correlation Distillation C. Pang et al.