Datasets:

NEST3D
/

dataset

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

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

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

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

from datasets import load_dataset

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

Downloading the Dataset

Option 1: Using Hugging Face Hub

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.

Last Updated: February 2026
Dataset Version: 1.0