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--- |
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language: |
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- en |
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license: |
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- cc-by-4.0 |
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tags: |
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- semantic-segmentation |
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- scene-understanding |
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- 3d-point-clouds |
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- multimodal |
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- drone-imagery |
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pretty_name: NEST3D - Sociable Weaver Nest 3D Dataset |
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--- |
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# NEST3D: A High-Resolution Multimodal Dataset of Sociable Weaver Tree Nests |
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## Dataset Description |
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NEST3D is a multimodal dataset of 104 sociable weaver nests, combining drone-based RGB and multispectral imagery with a semantically annotated 3D RGB point cloud. It captures trees hosting these nests through drone-based remote sensing, providing rich spatial and spectral information to benchmark and advance scene-level semantic segmentation methods for computer vision and ecological monitoring applications. |
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### Key Characteristics |
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- **Modality**: Multimodal (RGB imagery, multispectral bands, 3D point clouds) |
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- **Task**: Scene-level semantic segmentation |
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- **Scale**: Multiple tree-nest scenes with consistent spatial and spectral coverage |
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- **Annotation**: Point-level semantic labels for 3D point clouds |
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- **Data Source**: Drone-based RGB and multispectral imagery |
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- **Application Domain**: Ecological monitoring, wildlife management, 3d semantic segmenation, 3d reconstruction. |
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## Dataset Organization |
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The dataset is organized into modality-specific directories to support flexible access and reuse: |
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### Directory Structure |
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``` |
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NEST3D/ |
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├── train/ |
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│ ├── sample_001/ |
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│ │ ├── RGB/ # RGB drone images |
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│ │ │ ├── sample001_RGB_001.JPG |
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│ │ │ └── ... |
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│ │ ├── MS/ # Multispectral imagery |
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│ │ │ ├── Green/ |
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│ │ │ │ ├── sample001_G_001.TIF |
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│ │ │ │ └── ... |
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│ │ │ ├── Red/ |
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│ │ │ │ ├── sample001_R_001.TIF |
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│ │ │ │ └── ... |
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│ │ │ ├── Red_Edge/ |
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│ │ │ │ ├── sample001_RE_001.TIF |
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│ │ │ │ └── ... |
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│ │ │ └── NIR/ |
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│ │ │ ├── sample001_NIR_001.TIF |
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│ │ │ └── ... |
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│ │ └── sample001.npy # 3D point cloud with labels |
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│ └── sample_002/ |
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│ └── ... |
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│ |
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└── test/ |
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├── sample_084/ |
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│ ├── RGB/ |
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│ ├── MS/ |
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│ └── sample084.npy |
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└── ... |
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``` |
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### Data Modalities |
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#### 1. **RGB Imagery** |
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- Raw drone images from aerial acquisition |
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- Format: JPEG |
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- Organized by data split and scene identifier |
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- Example path: `train/sample_001/RGB/sample001_RGB_119.JPG` |
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#### 2. **Multispectral Imagery** |
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- Four spectral bands from the same acquisitions as RGB |
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- Organized into four band-specific folders: |
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- **Green (G)**: Green channel imagery |
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- **Red (R)**: Red channel imagery |
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- **Red Edge (RE)**: Red Edge channel for vegetation analysis |
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- **NIR**: Near-Infrared channel for vegetation health assessment |
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- Format: GeoTIFF (.TIF) |
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- Example paths: |
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- `train/sample_001/MS/Green/sample001_G_119.TIF` |
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- `train/sample_001/MS/Red/sample001_R_119.TIF` |
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- `train/sample_001/MS/Red_Edge/sample001_RE_119.TIF` |
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- `train/sample_001/MS/NIR/sample001_NIR_119.TIF` |
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#### 3. **3D Point Clouds** |
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- One NumPy file per scene containing the complete 3D reconstruction |
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- Format: `.npy` (NumPy binary format) |
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- Per-point attributes: `[x, y, z, r, g, b, label]` |
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- **x, y, z**: 3D spatial coordinates (meters) |
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- **r, g, b**: RGB color values (0-255) |
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- **label**: Semantic class label (integer) |
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- Example path: `train/sample_001/sample001.npy` |
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## Data Splits |
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The dataset is divided into fixed training and test sets: |
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- **Training Set**: Used for model training and development |
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- **Test Set**: Reserved for model evaluation and benchmarking |
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Each split contains a consistent collection of scenes to ensure reliable evaluation. |
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## Usage |
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### Loading 3D Point Clouds |
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```python |
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import numpy as np |
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# Load point cloud with semantic labels |
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point_cloud = np.load('train/sample_001/sample001.npy') |
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# Extract coordinates |
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xyz = point_cloud[:, :3] |
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# Extract colors |
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rgb = point_cloud[:, 3:6] |
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# Extract semantic labels |
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labels = point_cloud[:, 6] |
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``` |
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### Loading Multispectral Imagery |
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```python |
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from PIL import Image |
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import numpy as np |
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# Load a single band |
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green_band = np.array(Image.open('train/sample_001/MS/Green/sample001_G_001.TIF')) |
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# Load all four bands for a given image |
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green = np.array(Image.open('train/sample_001/MS/Green/sample001_G_001.TIF')) |
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red = np.array(Image.open('train/sample_001/MS/Red/sample001_R_001.TIF')) |
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red_edge = np.array(Image.open('train/sample_001/MS/Red_Edge/sample001_RE_001.TIF')) |
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nir = np.array(Image.open('train/sample_001/MS/NIR/sample001_NIR_001.TIF')) |
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# Stack into multiband image |
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multispectral = np.stack([green, red, red_edge, nir], axis=-1) |
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``` |
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### Using with Hugging Face Datasets Library |
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```python |
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from datasets import load_dataset |
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# Load the dataset |
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dataset = load_dataset('NEST3D/dataset') |
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``` |
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## Downloading the Dataset |
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### Option 1: Using Hugging Face Hub |
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```bash |
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pip install huggingface_hub |
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huggingface-cli download NEST3D/dataset --repo-type dataset --local-dir ./NEST3D |
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``` |
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## Dataset Information |
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- **Total Size**: |
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- **Number of Scenes**: 104 samples. Split into train/test as 83/21 |
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- **Modalities**: RGB, Multispectral (4 bands), 3D Point Clouds |
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- **Image Format**: JPEG (RGB), GeoTIFF (Multispectral) |
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- **Point Cloud Format**: NumPy arrays |
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- **Annotation Type**: Per-point semantic labels |
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## Acknowledgments |
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This work was funded by: |
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- **European Union's Horizon Europe** research and innovation programme through the Marie Skłodowska-Curie project **"WildDrone – Autonomous Drones for Nature Conservation"** (grant agreement no. 101071224) |
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- **EPSRC-funded** "Autonomous Drones for Nature Conservation Missions" grant (EP/X029077/1) |
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- **Swiss State Secretariat for Education, Research and Innovation (SERI)** under contract number 22.00280 |
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We extend our gratitude to our collaborators and field partners in Namibia for their invaluable support during data collection. |
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## Contact & Support |
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For questions, issues, or contributions, please visit the [dataset discussion forum](https://huggingface.co/datasets/NEST3D/dataset/discussions). |
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**Last Updated**: February 2026 |
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**Dataset Version**: 1.0 |
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