NEST3D: A High-Resolution Multimodal Dataset of Sociable Weaver Tree Nests
For ecologists and computer vision researchers, this dataset provides a high-resolution, multimodal resource for studying complex 3D structures in natural environments, enabling applications like nest volume estimation and species conservation.
This paper introduces NEST3D, a 1.4 TB multimodal drone dataset of 104 sociable weaver nest-bearing trees, including RGB and multispectral images, 3D point clouds, and semantic segmentation labels. Benchmarking with Point Transformer V3 achieved 86.35% mIoU, demonstrating strong performance while highlighting challenges for convolution-based methods.
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.