Change Detection in Urban Point Clouds: An Experimental Comparison with Simulated 3D Datasets
In the context of rapid urbanization, monitoring the evolution of cities is crucial. To do so, 3D change detection and characterization is of capital importance since, unlike 2D images, 3D data contain vertical information of utmost importance to monitoring city evolution (that occurs along both hor...
Main Authors: | Iris de Gélis, Sébastien Lefèvre, Thomas Corpetti |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2021-07-01
|
Series: | Remote Sensing |
Subjects: | |
Online Access: | https://www.mdpi.com/2072-4292/13/13/2629 |
Similar Items
-
Annotation Tool and Urban Dataset for 3D Point Cloud Semantic Segmentation
by: Muhammad Ibrahim, et al.
Published: (2021-01-01) -
Airborne LiDAR Strip Adjustment Method Based on Point Clouds with Planar Neighborhoods
by: Zhenxing Sun, et al.
Published: (2023-11-01) -
Airborne LiDAR Point Cloud Processing for Archaeology. Pipeline and QGIS Toolbox
by: Benjamin Štular, et al.
Published: (2021-08-01) -
Airborne Laser Scanning Point Cloud Classification Using the DGCNN Deep Learning Method
by: Elyta Widyaningrum, et al.
Published: (2021-02-01) -
Airborne LiDAR and Photogrammetric Point Cloud Fusion for Extraction of Urban Tree Metrics According to Street Network Segmentation
by: Weijun Yang, et al.
Published: (2021-01-01)