# How to Identify Good Superpixels for Deforestation Detection on Tropical Rainforests

arXiv: https://arxiv.org/abs/2409.04330 · HTML: https://mlsanity.com/paper/2409.04330/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 38 references that are in the map.
- First posted on arXiv 6 Sep 2024.

## Builds on (most cited first, 10 of 10)

- [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)
- [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)
- [Unsupervised Segmentation of Hyperspectral Remote Sensing Images with Superpixels](https://mlsanity.com/paper/2204.12296/) (2022)
- [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)
- [A Satellite Band Selection Framework for Amazon Forest Deforestation Detection Task](https://mlsanity.com/paper/2404.02659/) (2024)

## Built on by (most cited first, 3 of 3)

- [Exploring Superpixel Segmentation Methods in the Context of Citizen Science and Deforestation Detection](https://mlsanity.com/paper/2411.17922/) (2024)
- [Do Superpixel Segmentation Methods Influence Deforestation Image Classification?](https://mlsanity.com/paper/2510.04645/) (2025)
- [Learning Superpixel Ensemble and Hierarchy Graphs for Melanoma Detection](https://mlsanity.com/paper/2604.03710/) (2026)

## Abstract (arXiv)

The conservation of tropical forests is a topic of significant social and ecological relevance due to their crucial role in the global ecosystem. Unfortunately, deforestation and degradation impact millions of hectares annually, requiring government or private initiatives for effective forest monitoring. However, identifying deforested regions in satellite images is challenging due to data imbalance, image resolution, low-contrast regions, and occlusion. Superpixel segmentation can overcome these drawbacks, reducing workload and preserving important image boundaries. However, most works for remote sensing images do not exploit recent superpixel methods. In this work, we evaluate 16 superpixel methods in satellite images to support a deforestation detection system in tropical forests. We also assess the performance of superpixel methods for the target task, establishing a relationship with segmentation methodological evaluation. According to our results, ERS, GMMSP, and DISF perform best on UE, BR, and SIRS, respectively, whereas ERS has the best trade-off with CO and Reg. In classification, SH, DISF, and ISF perform best on RGB, UMDA, and PCA compositions, respectively. According to our experiments, superpixel methods with better trade-offs between delineation, homogeneity, compactness, and regularity are more suitable for identifying good superpixels for deforestation detection tasks.