NIBIO_UAV_tree_damage

🌟 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 📦🌲

🖥️ Existing Code and Model

The code for model inference, as described in the paper by Puliti and Astrup (2022), is available in the following GitHub repository:

🔗 GitHub Repository for Model Inference

This repository includes:

Inference Scripts: Scripts to apply the trained YOLOv5 model for detecting snow breakage at the single-tree level. 🌲

Pre-trained Models: Downloadable weights for reproducing results from the publication.

Example Workflows: Step-by-step guidance for running the model on your own UAV imagery. 🚁

Make sure to follow the repository’s documentation for setup instructions, dependencies, and usage examples. 💻

📜 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 🙌