Riverine Land Cover Mapping through Semantic Segmentation of Multispectral Point Clouds

2026-03-23

AI Summary

This study addresses the need for high-precision land cover mapping in riparian zones by introducing Point Transformer v2 to semantic segmentation of multispectral LiDAR point clouds. The proposed approach effectively integrates geometric structure with spectral features—specifically intensity and reflectance—to accurately distinguish land cover classes such as sand, gravel, low and high vegetation, forest, and water bodies. By employing a multi-dataset joint training strategy, the model achieves significantly enhanced generalization capability in scenarios with sparse annotations. Evaluated on the Oulanka River dataset, the method attains a mean Intersection over Union (mIoU) of 0.950, substantially outperforming baseline approaches that rely solely on geometric features, thereby demonstrating its high accuracy and robustness for riparian zone mapping.

Technology Category

• Computer Vision: Remote Sensing / Geospatial AI
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Machine Learning: Multi-instance/Multi-view Learning

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Security and Privacy: Data transparency and provenance

Abstract

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. We utilize the geometric and spectral information from the 3-channel LiDAR point cloud to map land cover classes, including sand, gravel, low vegetation, high vegetation, forest floor, and water. The PTv2 model was trained and evaluated on point cloud data from the Oulanka river in northern Finland using both geometry and spectral features. To improve the model's generalization in new riverine environments, we additionally investigate multi-dataset training that adds sparsely annotated data from an additional river dataset. Results demonstrated that using the full-feature configuration resulted in performance with a mean Intersection over Union (mIoU) of 0.950, significantly outperforming the geometry baseline. Other ablation studies revealed that intensity and reflectance features were the key for accurate land cover mapping. The multi-dataset training experiment showed improved generalization performance, suggesting potential for developing more robust models despite limited high-quality annotated data. Our work demonstrates the potential of applying transformer-based architectures to multispectral point clouds in riverine environments. The approach offers new capabilities for monitoring sediment transport and other river management applications.

Problem

• riverine land cover mapping
• semantic segmentation
• multispectral point clouds
• LiDAR
• geomorphic monitoring

Innovation

• Point Transformer v2
• multispectral LiDAR
• semantic segmentation
• riverine land cover mapping
• multi-dataset training