# Exploring Superpixel Segmentation Methods in the Context of Citizen Science and Deforestation Detection

arXiv: https://arxiv.org/abs/2411.17922 · HTML: https://mlsanity.com/paper/2411.17922/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 62 references that are in the map.
- First posted on arXiv 26 Nov 2024.

## Builds on (most cited first, 13 of 13)

- [SEEDS: Superpixels Extracted via Energy-Driven Sampling](https://mlsanity.com/paper/1309.3848/) (2013)
- [Superpixel Segmentation with Fully Convolutional Networks](https://mlsanity.com/paper/2003.12929/) (2020)
- [Superpixel Hierarchy](https://mlsanity.com/paper/1605.06325/) (2016)
- [Superpixel Segmentation Using Gaussian Mixture Model](https://mlsanity.com/paper/1612.08792/) (2016)
- [A comprehensive review and new taxonomy on superpixel segmentation](https://mlsanity.com/paper/2409.19179/) (2024)
- [Robust superpixels using color and contour features along linear path](https://mlsanity.com/paper/1903.07193/) (2019)
- [An Iterative Spanning Forest Framework for Superpixel Segmentation](https://mlsanity.com/paper/1801.10041/) (2018)
- [Superpixel Segmentation using Dynamic and Iterative Spanning Forest](https://mlsanity.com/paper/2007.04257/) (2020)
- [Efficient Multiscale Object-based Superpixel Framework](https://mlsanity.com/paper/2204.03533/) (2022)
- [ForestEyes Project: Conception, Enhancements, and Challenges](https://mlsanity.com/paper/2208.11687/) (2022)
- [How to Identify Good Superpixels for Deforestation Detection on Tropical Rainforests](https://mlsanity.com/paper/2409.04330/) (2024)
- [SIN:Superpixel Interpolation Network](https://mlsanity.com/paper/2110.08702/) (2021)
- [Sampling Strategies based on Wisdom of Crowds for Amazon Deforestation Detection](https://mlsanity.com/paper/2408.12381/) (2024)

## Built on by (most cited first, 1 of 1)

- [Do Superpixel Segmentation Methods Influence Deforestation Image Classification?](https://mlsanity.com/paper/2510.04645/) (2025)

## Abstract (arXiv)

Tropical forests play an essential role in the planet's ecosystem, making the conservation of these biomes a worldwide priority. However, ongoing deforestation and degradation pose a significant threat to their existence, necessitating effective monitoring and the proposal of actions to mitigate the damage caused by these processes. In this regard, initiatives range from government and private sector monitoring programs to solutions based on citizen science campaigns, for example. Particularly in the context of citizen science campaigns, the segmentation of remote sensing images to identify deforested areas and subsequently submit them to analysis by non-specialized volunteers is necessary. Thus, segmentation using superpixel-based techniques proves to be a viable solution for this important task. Therefore, this paper presents an analysis of 22 superpixel-based segmentation methods applied to remote sensing images, aiming to identify which of them are more suitable for generating segments for citizen science campaigns. The results reveal that seven of the segmentation methods outperformed the baseline method (SLIC) currently employed in the ForestEyes citizen science project, indicating an opportunity for improvement in this important stage of campaign development.