🌟 Introduction

This repository provides the data used in the research by Puliti and Astrup (2022) Automatic detection of snow breakage at single tree level using YOLOv5 applied to UAV imagery. International Journal of Applied Earth Observation and Geoinformation, 112, p.102946.

🌲 Scope of the Data

This dataset is intended for:🔍 Development and benchmarking of object detection models for individual trees and classification of trees based on their health.

Data is provided in the YOLO format with bounding box labels 📦🌲

📜 Citation

If you use this dataset, please give credit by citing the original paper:

@article{PULITI2022102946,
title = {Automatic detection of snow breakage at single tree level using YOLOv5 applied to UAV imagery},
journal = {International Journal of Applied Earth Observation and Geoinformation},
volume = {112},
pages = {102946},
year = {2022},
issn = {1569-8432},
doi = {https://doi.org/10.1016/j.jag.2022.102946},
url = {https://www.sciencedirect.com/science/article/pii/S1569843222001431},
author = {Stefano Puliti and Rasmus Astrup},
keywords = {Forest damage, Convolutional neural network, Deep-learning, Drones, Object detection}
}

⚖️ Licensing

📄 Please refer to the specific licenses below for details on how the data can be used.

🔑 Key Licensing Principles:

• ✅ You may access, use, and share the dataset and models freely.
• 🔄 Any derivative works (e.g., trained models, code for training, or prediction tools) must also be made publicly available under the same licensing terms.
• 🌍 These licenses promote collaboration and transparency, ensuring that research using this dataset benefits the broader scientific and open-source community 🙌