AI RESEARCH
Riverine Land Cover Mapping through Semantic Segmentation of Multispectral Point Clouds
arXiv CS.CV
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ArXi:2603.22230v1 Announce Type: new Accurate land cover mapping in riverine environments is essential for effective river management, ecological understanding, and geomorphic change monitoring. This study explores the use of Point Transformer v2 (PTv2), an advanced deep neural network architecture designed for point cloud data, for land cover mapping through semantic segmentation of multispectral LiDAR data in real-world riverine environments.