ReforesTree

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🌴 A dataset for estimating tropical forest biomass based on drone and field data

ReforesTree 최신버젼 다운로드

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# ReforesTree 🌴

We are excited to share the ReforesTree dataset! 🎉

We introduce the ReforesTree dataset in hopes of encouraging the fellow machine learning community to take on the challenge of developing low-cost, scalable, trustworthy and accurate solutions for monitoring, verification and reporting of tropical reforestation inventory.

#### This is a dataset for the following 6 agroforestry sites
In alphabetical order
1. _Carlos Vera Arteaga_
2. _Carlos Vera Guevara_
3. _Flora Pluas_
4. _Leonor Aspiazu_
5. _Manuel Macias_
6. _Nestor Macias_

## Dataset Components
For each site the data we publish consists of four components free for use:
1. 🛸 Raw drone RGB images _(see wwf_ecuador)_
2. 🌴 Hand measured tree parameters (diameter at breast height, species, biomass, and location) of every tree _(see field_data.csv)_
3. 🔲 Set of bounding boxes of trees for each site cleaned by hand and labeled as banana or not banana _(see annotations/cleaned)_
4. ↔️ Mappings of these bounding boxes with tree labels based on GPS location _(see mappings/final)_

## Usage

### Dataloader in PyTorch

Thanks to the torchgeo team, you can download the dataset through the [ReforesTree data loader](https://torchgeo.readthedocs.io/en/latest/api/datasets.html?highlight=reforestree#reforestree)

```python
from torchgeo.datasets import ReforesTree
ds = ReforesTree(root="data/reforestree/", download=True, checksum=True)
```

### Raw data

You can download the raw data [from dropbox](https://www.dropbox.com/scl/fo/qzlq48k06dbksvrl3kk1o/h?dl=0&rlkey=hpq3r63b1tbpqa02f5tyh8l6s) and put the "data" folder in the main repo. All processed data is available directly to use, but if you want to process it yourself, feel free to only download "www_ecuador" and "field_data.csv" and follow the tutorial below.

Alternatively, we are hosting a version of the dataset in [zenodo](https://zenodo.org/record/6813783).

## Tutorial
In the tutorial you'll find the steps to recreate (and hopefully improve) the dataset and how to use it.

Please read our paper [here](https://arxiv.org/abs/2201.11192).
For any questions, please reach out to gyri.reiersen@tum.de or david.dao@inf.eth.ch

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