Skip to content

๐ŸŒด A dataset for estimating tropical forest biomass based on drone and field data

Notifications You must be signed in to change notification settings

daviddao/ReforesTree

ย 
ย 

Folders and files

NameName
Last commit message
Last commit date

Latest commit

ย 

History

12 Commits
ย 
ย 
ย 
ย 
ย 
ย 
ย 
ย 

Repository files navigation

ReforesTree ๐ŸŒด

We are excited to share the ReforesTree dataset! ๐ŸŽ‰

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.

illustration of all sites

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. ๐Ÿ›ธ Raw drone RGB images (see wwf_ecuador)
  2. ๐ŸŒด Hand measured tree parameters (diameter at breast height, species, biomass, and location) of every tree (see field_data.csv)
  3. ๐Ÿ”ฒ Set of bounding boxes of trees for each site cleaned by hand and labeled as banana or not banana (see annotations/cleaned)
  4. โ†”๏ธ Mappings of these bounding boxes with tree labels based on GPS location (see mappings/final)

You can download the data from dropbox 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.

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. For any questions, please reach out to gyri.reiersen@tum.de or david.dao@inf.eth.ch

About

๐ŸŒด A dataset for estimating tropical forest biomass based on drone and field data

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages

  • Jupyter Notebook 57.5%
  • Python 42.5%