Unsupervised Class-Guided Synthetic-to-Real Domain Adaptation for Plot-Level Forest UAV LiDAR Semantic Segmentation

Fine-grained tree-species segmentation in UAV RGB imagery using parameter-efficient segment anything adaptation

A Comparative Study of Deep Learning and Unsupervised Segmentation Methods for Individual Tree Delineation from LiDAR Point Clouds

Abstract. Individual tree segmentation from LiDAR is central to automated inventory, yet many evaluations cover only one forest type or one methodological family. It also remains unclear whether point-level and instance-level metrics rank methods in the same order, which complicates operational choices. We therefore harmonise preprocessing, a binary tree versus non-tree semantic task, and a joint evaluation protocol. Representative unsupervised (graph-based TreeIso; region-growing TreeX) and deep learning (vote-based TreeLearn; mask-based ForestFormer3D) pipelines are compared on both public benchmarks and a fused unmanned aerial and mobile laser scanning benchmark from rugged subtropical Hong Kong woodland called HKTrees. Performance is strongest on regular coniferous plots. Broadleaved heterogeneity and crown overlap lower instance recall relative to semantic scores. HKTrees is the hardest regime and shows pronounced domain shift. Point-level and instance-level rankings are not always aligned. We discuss trade-offs in annotation cost, generalisation, and interpretability and outline expansion of HKTrees dataset for larger-scale training.

Synthetic Forest: A UAV Laser Scanning Benchmark Dataset for Individual Tree Segmentation, Classification, and Wood Volume Estimation

Abstract. Accurate tree-level analysis in forests via LiDAR scanning is essential for biomass estimation, canopy structure assessment, and carbon monitoring, yet remains constrained by the scarcity of large-scale annotated LiDAR datasets and the high cost of manual annotation. To address this, we present a novel approach that integrates 3D tree models with UAV-borne LiDAR simulation to generate synthetic forest point clouds with comprehensive annotations. Our approach generates diverse woodland, open, and closed forest structures, producing Synthetic Forest, a benchmark dataset of three 1 ha scenes containing 38–47 million points each, with densities of 3302–3862 points/m² and average spacing of 2 cm. Each scene contains between 70 and 216 individual trees, along with understory vegetation, deadwood, stumps, rocks, and bushes, all automatically annotated with semantic classification IDs, instance IDs, and tree IDs for volume estimation. The proposed pipeline provides automated, error-free ground truth for leaf-wood classification, instance segmentation, and wood volume estimation. We provide guidelines for generating forest plots and utilizing the datasets for diverse forestry tasks. By eliminating the need for costly field data collection, our pipeline offers scalable, customizable synthetic datasets that accelerate forest inventory. The Synthetic Forest dataset is publicly released via Zenodo (DOI: 10.5281/zenodo.17568131), enabling reproducible research and supporting further developments in forest monitoring and management.

Uncertainty-Guided Multi-Center Prototype Alignment for Cross-Domain Few-Shot Hyperspectral Image Classification

Accurate hyperspectral image analysis plays a critical role in environmental monitoring, precision agriculture, and urban mapping, yet acquiring large-scale annotated datasets for newly emerging scenes and sensors remains challenging. Cross-domain few-shot hyperspectral image classification addresses this bottleneck by transferring knowledge from a labeled source domain to a sparsely annotated target domain. Existing prototype-based approaches commonly use a single-mean prototype per class, which is often inadequate for target classes with spectral–spatial heterogeneity, intra-class dispersion, and unstable episode-level statistics. Consequently, the resulting alignment reference may fail to accurately characterize class structure, thereby exacerbating negative transfer under domain shift. To address this issue, we propose a plug-and-play prototype-stability module that combines Uncertainty-Guided Clustered Alignment (UGCA) with Center Regularization. UGCA identifies hard classes using a class-level dispersion proxy and dynamically constructs multi-center prototypes to better capture intra-class heterogeneity beyond the single-mean assumption. Meanwhile, Center Regularization adds a lightweight compactness constraint on query embeddings to reduce prototype drift under sparse supervision. Experiments on three cross-domain tasks demonstrate the effectiveness of the proposed method. Compared with the reproduced MLPA baseline over 10 independent runs, the proposed method improves the mean OA from 69.21 ± 2.71%, 81.90 ± 3.45%, and 76.92 ± 1.10% to 71.24 ± 3.39%, 83.73 ± 3.89%, and 78.24 ± 1.55% on the Indian Pines (IP), University of Pavia (UP), and Houston (HT) target domains, respectively. Furthermore, cross-framework insertion into Gia-CFSL further verifies that the module is host-agnostic across prototype-driven CD-FSL frameworks and improves prototype-based hyperspectral image analysis without changing the inference pipeline. These results indicate that improving prototype quality is a critical and complementary dimension for robust cross-domain few-shot hyperspectral classification under sparse supervision.

Explicit Geometry-Reflectance Domain Shift Modeling for Robust LiDAR Segmentation in Adverse Weather

Weakly supervised semantic segmentation of 3D point clouds for coal mine roadways: Method and application

With advantages in anti-interference and active sensing, Light Detection and Ranging (LiDAR) has become a primary modality for environmental perception in underground coal mines characterized by inadequate illumination and high dust concentrations. However, raw point clouds lack semantic attributes, which limits their direct application in scene understanding for digital mines. Although fully supervised deep learning methods for point cloud semantic segmentation achieve high performance, they rely heavily on labor-intensive point-wise annotations, incurring high costs and potential human error. To address this, this paper proposes a weakly supervised semantic segmentation framework specifically designed for coal mine roadways. The framework establishes a comprehensive workflow spanning from data acquisition to sparse-supervised training. By leveraging sparse labels to drive model optimization, the method maintains segmentation performance while significantly reducing annotation costs, achieving a strategic balance between engineering precision and deployment overhead. Experimental results demonstrate that using only 0.1% sparse annotations, the proposed method achieves an Overall Accuracy (OA) of 91.64% and a mean Intersection over Union (mIoU) of 78.83%. Compared to fully supervised models, it achieves a 2-3 times improvement in annotation efficiency with a precision loss of approximately 7%. The segmented structural point clouds are utilized for roadway deformation monitoring and parametric geometric reconstruction. This supports a comprehensive digital twin framework that achieves high-fidelity modeling and centimeter-level deformation early-warning. This research confirms that the proposed technology provides a cost-effective and efficient path for scene understanding in intelligent mines, demonstrating substantial engineering value and potential for industrial promotion.