LiDAR Image

LiDAR image processing focuses on extracting meaningful information from point cloud data, often represented as images, for various applications. Current research emphasizes the use of deep learning, particularly convolutional neural networks (like U-Net and YOLOv8) and transformer-based architectures, to perform tasks such as segmentation (identifying buildings, trees, or archaeological features) and classification of landscape features. These techniques are improving the efficiency and accuracy of tasks ranging from automated mapping and ecological monitoring to autonomous driving, where LiDAR's depth information is proving valuable for robust navigation. The development of methods to handle sensor variations and fuse LiDAR data with other modalities, like RGB imagery and thermal data, is a key area of ongoing investigation.