用高度嵌入保留垂直结构，提升冻土融化地图精度
Preserving Vertical Structure in 3D-to-2D Projection for Permafrost Thaw Mapping
- 引入学习型高度嵌入的投影解码器，区分地面与树冠信号
- 在阿拉斯加森林区实现厘米级冻土深度图预测，优于传统方法
- 适合无人机遥感监测复杂植被区冻土退化，可规模化部署
从航空激光雷达预测冻土融化需将3D点云特征投影至2D预测网格，但简单聚合方法会破坏森林环境中地面、灌木层和树冠层的关键垂直结构信息。本文提出一种带有学习高度嵌入的投影解码器，实现高度依赖的特征变换，使网络能区分地面信号与树冠回波。结合分层采样策略确保各植被层均被保留，该方法有效维持了预测地下条件所需的垂直信息。模型采用Point Transformer V3编码器，基于无人机采集的阿拉斯加内陆苔原区域激光雷达数据，生成密集冻土融化深度图。实验表明，z-分层投影显著优于标准平均法，尤其在垂直结构复杂的区域表现更优。该方法可实现高分辨率、可扩展的冻土退化监测，适用于易部署的无人机平台。
原文摘要 · Abstract (English)
Forecasting permafrost thaw from aerial lidar requires projecting 3D point cloud features onto 2D prediction grids, yet naive aggregation methods destroy the vertical structure critical in forest environments where ground, understory, and canopy carry distinct information about subsurface conditions. We propose a projection decoder with learned height embeddings that enable height-dependent feature transformations, allowing the network to differentiate ground-level signals from canopy returns. Combined with stratified sampling that ensures all forest strata remain represented, our approach preserves the vertical information critical for predicting subsurface conditions. Our approach pairs this decoder with a Point Transformer V3 encoder to predict dense thaw depth maps from drone-collected lidar over boreal forest in interior Alaska. Experiments demonstrate that z-stratified projection outperforms standard averaging-based methods, particularly in areas with complex vertical vegetation structure. Our method enables scalable, high-resolution monitoring of permafrost degradation from readily deployable UAV platforms.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。