https://github.com/kumarrobotics/treescope
https://github.com/kumarrobotics/treescope
Last synced: about 1 year ago
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- Host: GitHub
- URL: https://github.com/kumarrobotics/treescope
- Owner: KumarRobotics
- Created: 2023-07-21T18:45:55.000Z (almost 3 years ago)
- Default Branch: main
- Last Pushed: 2024-03-01T15:45:22.000Z (over 2 years ago)
- Last Synced: 2025-03-25T09:02:46.031Z (about 1 year ago)
- Language: Python
- Size: 17.3 MB
- Stars: 41
- Watchers: 4
- Forks: 6
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
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README
# 🌲 TreeScope: An Agricultural Robotics Dataset for LiDAR-Based Mapping of Trees in Forests and Orchards
## Overview
[](https://www.youtube.com/watch?v=750oL-VsSIM)
**TreeScope** is a robotics dataset for precision agriculture and forestry addressing the counting and mapping of trees in forestry and orchards. **TreeScope** provides LiDAR data from agricultural environments collected with robotics platforms, such as UAV and mobile robot platforms carried by vehicles and human operators. We provide ground-truth data for semantic segmentation and diameter estimation with over 1,800 manually annotated semantic labels for tree stems and field-measured tree diameters. We share benchmark scripts for these tasks that researchers may use to evaluate the accuracy of their algorithms.
**TreeScope** processed data, raw data, and code are available to [download](https://test.treescope.org).
For more information about our dataset, please visit [https://treescope.org](https://treescope.org/) or watch our [video](https://youtu.be/GgV1PmLEFeI).
For detailed instructions on how to use this repository, please refer to this [step-by-step tutorial](https://docs.google.com/document/d/1j11YdxhWRJNfgbbc-5mdX1gpp7nh9M_Iu40lXpW7L3g/edit?usp=sharing)
## Converting Labels
For converting H5 labels into 2D range images:
```
python3 semantic_segmentation/h5-to-labels.py --file --output
```
For converting labeled 2D range images into H5 labels:
```
python3 semantic_segmentation/full_data_preprocessor.py -D
python3 semantic_segmentation/labels-to-h5.py -D
```
## Diameter Estimation Benchmarks
For calculating root-mean-square error of diameter estimation results compared to ground-truth:
```
python3 diameter_estimation/evaluate_dbh_rmse.py [output.csv]
```
## Semantic Segmentation Benchmarks
For calculating IoU of inference point cloud (projected to 2D range image) compared to ground-truth:
```
python3 semantic_segmentation/evaluate_iou.py stacked_image.png .yaml
python3 semantic_segmentation/evaluate_iou.py ground_truth.png pred.png .yaml
```
# Citation
You can access the paper from [arXiv](https://arxiv.org/abs/2310.02162). To
cite our work, please use:
```
@misc{cheng2023treescope,
title={TreeScope: An Agricultural Robotics Dataset for LiDAR-Based Mapping of Trees in Forests and Orchards},
author={Derek Cheng and Fernando Cladera Ojeda and Ankit Prabhu and Xu Liu and Alan Zhu and Patrick Corey Green and Reza Ehsani and Pratik Chaudhari and Vijay Kumar},
year={2023},
eprint={2310.02162},
archivePrefix={arXiv},
primaryClass={cs.RO}
}
```
## Contributions or Questions?
Please [fill-out an issue](https://github.com/KumarRobotics/treescope/issues) if you have any questions.
Do not hesitate to [send your pull request](https://github.com/KumarRobotics/treescope/pulls).