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English(EN) Super-resolution of airborne laser scanning point clouds for forest inventory

用于森林清单的机载激光扫描点云超分辨率

研究人员开发了一种名为 3D Forest Super Resolution (3DFSR) 的深度学习模型,以增强机载激光扫描 (ALS) 点云,从而实现更准确的森林清单。这种基于体素的、具有 U-Net 架构的 CNN 提高了点密度并减少了 ALS 数据中的噪声,从而改善了树干定位和尺寸估算。实验表明,与传统方法相比,树干检测 F1 分数和胸径 (DBH) 估算精度有了显著提高。 AI

影响 提高了从稀疏 LiDAR 数据进行森林清单和树木测量的准确性。

排序理由 该集群包含一篇 arXiv 预印本,详细介绍了用于点云超分辨率的新深度学习模型。

在 arXiv cs.CV 阅读 →

AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →

用于森林清单的机载激光扫描点云超分辨率

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该集群包含一篇 arXiv 预印本,详细介绍了用于点云超分辨率的新深度学习模型。
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报道来源 [2]

  1. arXiv cs.CV TIER_1 English(EN) · Jinyuan Shao, Sangyoong Park, Chunxi Zhao, Ayman Habib, Songlin Fei ·

    面向森林清单的机载激光扫描点云超分辨率重建

    arXiv:2605.02201v1 Announce Type: new Abstract: Airborne Laser Scanning (ALS) can collect point clouds across large areas, enabling large-scale forest inventory. However, ALS point clouds are sparse and noisy, resulting in inaccurate individual-tree-level forest inventory, such a…

  2. arXiv cs.CV TIER_1 English(EN) · Songlin Fei ·

    面向森林清单的机载激光扫描点云超分辨率重建

    Airborne Laser Scanning (ALS) can collect point clouds across large areas, enabling large-scale forest inventory. However, ALS point clouds are sparse and noisy, resulting in inaccurate individual-tree-level forest inventory, such as stem localization and tree size estimation. To…