3D LULC classification using multispectral LiDAR and deep learning: current and prospective schemes

TL;DR

This paper introduces a new multispectral LiDAR dataset and evaluates deep learning models for 3D land cover classification aligned with national schemes, demonstrating improved accuracy with multispectral data.

Contribution

It presents a new benchmark dataset, NMCA-aligned classification schemes, and evaluates state-of-the-art deep learning models for multispectral LiDAR-based LULC classification.

Findings

Point Transformer V3 achieves 79.4% mIoU at L1 and 58.9% at L2.

Multispectral information improves performance over geometry-only inputs.

The Loosdorf-MSL dataset provides a new benchmark for LULC mapping.

Abstract

Land Use Land Cover (LULC) classification is essential for national 3D mapping, geospatial analysis, and sustainable planning. Multispectral (MS) LiDAR provides synchronized spatial-spectral information, and deep learning (DL) enables 3D point cloud semantic segmentation; however, adoption is limited by the lack of publicly available urban and suburban MS LiDAR datasets aligned with National Mapping and Cadastral Agencies (NMCAs) classification schemes. This study addresses these gaps by introducing L1 and L2 NMCA-aligned LULC classification schemes and a new benchmark MS LiDAR dataset. We evaluate seven state-of-the-art DL models and perform spectral ablation studies at both levels of detail. Results show that Point Transformer V3 achieves the best performance, with mIoU of 79.4% (L1, 8 classes) and 58.9% (L2, 20 classes) using a dual-wavelength LiDAR system (532 nm and 1064 nm). Ablation results show that multispectral information improves performance over geometry-only inputs, with gains of 1.1 percentage points at L1 and 7.8 points at L2. These results highlight the value of LiDAR reflectance for fine-grained material discrimination and support the evolution of NMCA LULC schemes toward higher semantic detail. The Loosdorf-MSL dataset contributes a new benchmark for consistent national and international LULC mapping.