---
language:
- en
license:
- cc-by-4.0
tags:
- semantic-segmentation
- scene-understanding
- 3d-point-clouds
- multimodal
- drone-imagery
pretty_name: NEST3D - Sociable Weaver Nest 3D Dataset
---

# NEST3D: A High-Resolution Multimodal Dataset of Sociable Weaver Tree Nests

![NEST3D Workflow](./workflow.png)

## Dataset Description

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.

### Key Characteristics

- **Modality**: Multimodal (RGB imagery, multispectral bands, 3D point clouds)
- **Task**: Scene-level semantic segmentation
- **Scale**: Multiple tree-nest scenes with consistent spatial and spectral coverage
- **Annotation**: Point-level semantic labels for 3D point clouds
- **Data Source**: Drone-based RGB and multispectral imagery
- **Application Domain**: Ecological monitoring, wildlife management, 3d semantic segmenation, 3d reconstruction.

## Dataset Organization

The dataset is organized into modality-specific directories to support flexible access and reuse:

### Directory Structure

```
NEST3D/
├── train/
│   ├── sample_001/
│   │   ├── RGB/                    # RGB drone images
│   │   │   ├── sample001_RGB_001.JPG
│   │   │   └── ...
│   │   ├── MS/                     # Multispectral imagery
│   │   │   ├── Green/
│   │   │   │   ├── sample001_G_001.TIF
│   │   │   │   └── ...
│   │   │   ├── Red/
│   │   │   │   ├── sample001_R_001.TIF
│   │   │   │   └── ...
│   │   │   ├── Red_Edge/
│   │   │   │   ├── sample001_RE_001.TIF
│   │   │   │   └── ...
│   │   │   └── NIR/
│   │   │       ├── sample001_NIR_001.TIF
│   │   │       └── ...
│   │   └── sample001.npy            # 3D point cloud with labels
│   └── sample_002/
│       └── ...
│
└── test/
    ├── sample_084/
    │   ├── RGB/
    │   ├── MS/
    │   └── sample084.npy
    └── ...
```

### Data Modalities

#### 1. **RGB Imagery**
- Raw drone images from aerial acquisition
- Format: JPEG
- Organized by data split and scene identifier
- Example path: `train/sample_001/RGB/sample001_RGB_119.JPG`

#### 2. **Multispectral Imagery**
- Four spectral bands from the same acquisitions as RGB
- Organized into four band-specific folders:
  - **Green (G)**: Green channel imagery
  - **Red (R)**: Red channel imagery
  - **Red Edge (RE)**: Red Edge channel for vegetation analysis
  - **NIR**: Near-Infrared channel for vegetation health assessment
- Format: GeoTIFF (.TIF)
- Example paths:
  - `train/sample_001/MS/Green/sample001_G_119.TIF`
  - `train/sample_001/MS/Red/sample001_R_119.TIF`
  - `train/sample_001/MS/Red_Edge/sample001_RE_119.TIF`
  - `train/sample_001/MS/NIR/sample001_NIR_119.TIF`

#### 3. **3D Point Clouds**
- One NumPy file per scene containing the complete 3D reconstruction
- Format: `.npy` (NumPy binary format)
- Per-point attributes: `[x, y, z, r, g, b, label]`
  - **x, y, z**: 3D spatial coordinates (meters)
  - **r, g, b**: RGB color values (0-255)
  - **label**: Semantic class label (integer)
- Example path: `train/sample_001/sample001.npy`

## Data Splits

The dataset is divided into fixed training and test sets:

- **Training Set**: Used for model training and development
- **Test Set**: Reserved for model evaluation and benchmarking

Each split contains a consistent collection of scenes to ensure reliable evaluation.

## Usage

### Loading 3D Point Clouds

```python
import numpy as np

# Load point cloud with semantic labels
point_cloud = np.load('train/sample_001/sample001.npy')

# Extract coordinates
xyz = point_cloud[:, :3]

# Extract colors
rgb = point_cloud[:, 3:6]

# Extract semantic labels
labels = point_cloud[:, 6]
```

### Loading Multispectral Imagery

```python
from PIL import Image
import numpy as np

# Load a single band
green_band = np.array(Image.open('train/sample_001/MS/Green/sample001_G_001.TIF'))

# Load all four bands for a given image
green = np.array(Image.open('train/sample_001/MS/Green/sample001_G_001.TIF'))
red = np.array(Image.open('train/sample_001/MS/Red/sample001_R_001.TIF'))
red_edge = np.array(Image.open('train/sample_001/MS/Red_Edge/sample001_RE_001.TIF'))
nir = np.array(Image.open('train/sample_001/MS/NIR/sample001_NIR_001.TIF'))

# Stack into multiband image
multispectral = np.stack([green, red, red_edge, nir], axis=-1)
```

### Using with Hugging Face Datasets Library

```python
from datasets import load_dataset

# Load the dataset
dataset = load_dataset('NEST3D/dataset')
```

## Downloading the Dataset

### Option 1: Using Hugging Face Hub
```bash
pip install huggingface_hub

huggingface-cli download NEST3D/dataset --repo-type dataset --local-dir ./NEST3D
```

## Dataset Information

- **Total Size**: 
  - **Number of Scenes**: 104 samples. Split into train/test as 83/21
- **Modalities**: RGB, Multispectral (4 bands), 3D Point Clouds
- **Image Format**: JPEG (RGB), GeoTIFF (Multispectral)
- **Point Cloud Format**: NumPy arrays
- **Annotation Type**: Per-point semantic labels

## Acknowledgments

This work was funded by:

- **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)
- **EPSRC-funded** "Autonomous Drones for Nature Conservation Missions" grant (EP/X029077/1)
- **Swiss State Secretariat for Education, Research and Innovation (SERI)** under contract number 22.00280

We extend our gratitude to our collaborators and field partners in Namibia for their invaluable support during data collection.

## Contact & Support

For questions, issues, or contributions, please visit the [dataset discussion forum](https://huggingface.co/datasets/NEST3D/dataset/discussions).


**Last Updated**: February 2026  
**Dataset Version**: 1.0