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README.md

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-
-# 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
-```
-
-### Option 2: Direct Download
-Visit [https://huggingface.co/datasets/NEST3D/dataset](https://huggingface.co/datasets/NEST3D/dataset) and download files directly.
-
-## Citation
-
-If you use this dataset in your research, please cite:
-
-```bibtex
-@dataset{NEST3D,
-  title={NEST3D: A High-Resolution Multimodal Dataset of Sociable Weaver Tree Nests},
-  organization={NEST3D},
-  url={https://huggingface.co/datasets/NEST3D},
-  year={2026}
-}
-```
-
-## License
-
-This dataset is released under the Creative Commons Attribution 4.0 International (CC-BY-4.0) License. You are free to use, distribute, and adapt the dataset as long as you provide appropriate attribution.
-
-See [LICENSE](LICENSE) for details.
-
-## Dataset Information
-
-- **Total Size**: 
-- **Number of Scenes**: 104 samples
-- **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).
-
-## Disclaimer
-
-The NEST3D dataset is provided as-is for research and development purposes. Users are responsible for ensuring their use complies with all applicable laws and regulations, particularly regarding ecological monitoring and wildlife protection.
-
----
-
-**Last Updated**: February 2026  
-**Dataset Version**: 1.0