Deep learning (DL) models combined with mobile laser scanning (MLS) datasets have demonstrated immense potential for vegetation segmentation. However, restricted performance and inconsistent behavior across datasets by generic DL models offer notable concerns. Furthermore, to capture the characteristic distribution of vegetation points toward effective segregation, a dedicated model for vegetation segmentation is essential. In addition, with curated class-specific DL models being conceptualized, the same is indispensable for vegetation. To address this problem, we propose a novel DL architecture, green segmentation network (GreenSegNet), tailored for vegetation segmentation from MLS point cloud data. Toward a comprehensive assessment, GreenSegNet has been investigated on MLS datasets from three study sites, Chandigarh, Toronto3D, and Kerala. GreenSegNet has illustrated state of the art (SOTA) as well as consistent segmentation performance across all the datasets. GreenSegNet has achieved mean intersection over union (mIoU) as follows: Chandigarh 96.43%, Toronto3D 92.70%, and Kerala 90.16%. In addition, with less than one million parameters, the architecture is the most efficient with respect to the number of parameters among the representative DL models. The associated ablation studies conform to the effectiveness of GreenSegNet. Unlike other SOTA models, GreenSegNet is found robust across different datasets and terrains.

中文翻译：

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GreenSegNet：一种根据 MLS 数据进行城市植被分割的新型深度学习架构


深度学习（DL）模型与移动激光扫描（MLS）数据集的结合已经证明了植被分割的巨大潜力。然而，通用深度学习模型在数据集中的性能受限和行为不一致带来了值得注意的问题。此外，为了捕获植被点的特征分布以实现有效隔离，植被分割的专用模型是必不可少的。此外，随着特定类别的深度学习模型的概念化，这对于植被来说也是必不可少的。为了解决这个问题，我们提出了一种新颖的深度学习架构——绿色分割网络（GreenSegNet），专为 MLS 点云数据的植被分割而定制。为了进行全面评估，GreenSegNet 对来自昌迪加尔、Toronto3D 和喀拉拉邦三个研究地点的 MLS 数据集进行了调查。 GreenSegNet 展示了最先进的技术 (SOTA) 以及所有数据集的一致分割性能。 GreenSegNet 已实现联合平均交集 (mIoU) 如下：昌迪加尔 96.43%、多伦多 3D 92.70% 和喀拉拉邦 90.16%。此外，该架构的参数少于一百万个，就参数数量而言是代表性深度学习模型中最有效的。相关的消融研究符合 GreenSegNet 的有效性。与其他 SOTA 模型不同，GreenSegNet 在不同的数据集和地形上都具有鲁棒性。