CHANGE DETECTION BETWEEN DIGITAL SURFACE MODELS FROM AIRBORNE LASER SCANNING AND DENSE IMAGE MATCHING USING CONVOLUTIONAL NEURAL NETWORKS

9 citations as recorded by crossref.

1. Pixel-Level and Object-Level Combined 3D Building Change Detection Method 志. 张 https://doi.org/10.12677/GST.2023.113024
2. Automatic 3D Multiple Building Change Detection Model Based on Encoder–Decoder Network Using Highly Unbalanced Remote Sensing Datasets M. Gomroki et al. https://doi.org/10.1109/JSTARS.2023.3328561
3. ME-CPT: Multitask Enhanced Cross-Temporal Point Transformer for Urban 3-D Change Detection L. Zhang et al. https://doi.org/10.1109/TGRS.2026.3661736
4. Transformer-based multimodal change detection with multitask consistency constraints B. Liu et al. https://doi.org/10.1016/j.inffus.2024.102358
5. Three Dimensional Change Detection Using Point Clouds: A Review A. Kharroubi et al. https://doi.org/10.3390/geomatics2040025
6. Detecting Building Changes between Airborne Laser Scanning and Photogrammetric Data Z. Zhang et al. https://doi.org/10.3390/rs11202417
7. Object-based 3D building change detection using point-level change indicators L. Zhang et al. https://doi.org/10.1016/j.jag.2023.103293
8. An object based framework for building change analysis using 2D and 3D information of high resolution satellite images H. Mohammadi & F. Samadzadegan https://doi.org/10.1016/j.asr.2020.05.041
9. Analysis and Verification of Building Changes Based on Point Clouds from Different Sources and Time Periods U. Marmol & N. Borowiec https://doi.org/10.3390/rs15051414