Sociable weaver nests function as complex ecological structures offering thermoregulatory microhabitats and sustaining diverse species; however, datasets used in prior studies lack fine-grained 3D structural detail. Producing usable and accurate 3D weaver nest data is challenging due to their irregular geometry and integration with complex host vegetation. We bridge this gap with an open-access, 1.4 TB multimodal drone dataset of 104 nest-bearing trees, comprising 27,945 RGB images, 111,780 multispectral images, approximately 781 million 3D points, and expert-annotated semantic segmentation labels. We benchmark semantic segmentation using KPConv, RandLA-Net, and Point Transformer V3, with PT-v3 achieving an mIoU of 86.35% on the test set. While the results demonstrate strong performance for transformer-based and point-wise methods, they also highlight architecture-dependent challenges, particularly for convolution-based approaches such as KPConv. By uniquely combining spectral, spatial, and structural information, the presented dataset advances 3D reconstruction, segmentation, and classification algorithms, enabling ecological applications from nest volume estimation to species conservation, and serves as a demanding benchmark that exposes architecture-dependent performance under extreme class imbalance.

翻译：群织鸟巢作为复杂的生态结构，不仅提供体温调节微栖息地，还维系着多种物种的生存；然而，现有研究使用的数据集缺乏精细的三维结构细节。由于鸟巢几何形态不规则且与复杂的宿主植被融为一体，生成可用且精确的三维织巢鸟数据极具挑战性。为填补这一空白，我们构建了一个开放获取的1.4 TB多模态无人机数据集，包含104棵有巢树木的27,945张RGB图像、111,780张多光谱图像、约7.81亿个三维点云，以及专家标注的语义分割标签。我们采用KPConv、RandLA-Net和Point Transformer V3进行语义分割基准测试，其中PT-v3在测试集上的平均交并比（mIoU）达到86.35%。结果表明，基于Transformer和逐点的方法表现出色，但也凸显了依赖特定架构的挑战，尤其是基于卷积的方法（如KPConv）。本数据集通过独特融合光谱、空间和结构信息，推动了三维重建、分割和分类算法的发展，支持从巢体积估算到物种保护的生态应用，同时作为一项高难度基准，揭示了在极端类别不平衡条件下模型性能对架构的依赖性。