Efficient and Lightweight Semantic Segmentation Network for Land Cover Point Cloud With Local-Global Feature Fusion

• Published in: IEEE transactions on geoscience and remote sensing Vol. 63; pp. 1 - 13
• Main Authors: Hu, Hong, Cai, Lantian, Kang, Ruihong, Wu, Yanlan, Wang, Chunlin
• Format: Journal Article
• Language: English
• Published: New York IEEE 2025; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Cognitive ability; Computer applications; Computing costs; Convolution; Data mining; Datasets; Deep learning; Feature extraction; Feature fusion; Floating point arithmetic; Image segmentation; Inference; Land cover; land cover semantic segmentation; Land surface; point cloud; Point cloud compression; Semantic segmentation; Semantics; Three-dimensional displays; Training; Transformers; Urban planning; VEF-Net

Utilizing deep learning techniques to extract high-precision features from point clouds is essential for accurately capturing land cover information, which is instrumental in urban planning and environmental conservation. Despite delivering high-accuracy outcomes in the semantic segmentation of exte...