README.md

---
license: apache-2.0
task_categories:
- image-feature-extraction
language:
- en
tags:
- climate
pretty_name: Domain-Adaptive Regression for Forest Monitoring
size_categories:
- 100M<n<1B
---



# The DRIFT (Domain-Adaptive Regression for Forest Monitoring) dataset

Dataset download link: https://sid.erda.dk/share_redirect/f1Hmpeh6O2

Project page: https://dgominski.github.io/drift/

GitHub page: https://github.com/sizhuoli/Domain_adaptive_regression_with_ordered_embedding_space

Publication: **ECCV 2024 proceeding**: Get Your Embedding Space in Order: Domain-Adaptive Regression for Forest Monitoring (https://arxiv.org/abs/2405.00514)

------------------------

## Description

The DRIFT dataset includes **25k** image patches collected in five European countries sourced from aerial and nanosatellite image archives. 

Each image patch is associated with **3** target variables to predict:

1. Canopy height: average height value for pixels containing woody vegetation.

2. Tree count: number of overstory (visible from an overhead perspective) trees in the images.

3. Tree cover fraction: percentage of the image being covered by overstory tree crowns.


The DRIFT dataset includes significant shifts between label and visual distributions due to sensor and area differences. 

Furthermore, vegetation tends to grow to fit the local climate, therefore introducing concept drift in the data: same tree species may appear differently in different subsets. The label distribution also varies among different subsets (countries).


## Applications

The dataset is a good choice for:

* image-level regression

* domain adaption for regression

* remote sensing for forest applications



## Citation:

```
@InProceedings{10.1007/978-3-031-72980-5_6,
author="Li, Sizhuo and Gominski, Dimitri and Brandt, Martin and Tong, Xiaoye and Ciais, Philippe",
editor="Leonardis, Ale{\v{s}} and Ricci, Elisa and Roth, Stefan and Russakovsky, Olga and Sattler, Torsten and Varol, G{\"u}l",
title="Get Your Embedding Space in Order: Domain-Adaptive Regression for Forest Monitoring",
booktitle="Computer Vision -- ECCV 2024",
year="2024",
publisher="Springer Nature Switzerland",
address="Cham",
pages="94--111",
isbn="978-3-031-72980-5"
}