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

TL;DR

This paper evaluates 22 superpixel segmentation methods on remote sensing images to identify the most suitable techniques for citizen science deforestation monitoring, revealing seven methods that outperform the current baseline in the ForestEyes project.

Contribution

It provides a comprehensive analysis of superpixel segmentation techniques specifically tailored for citizen science applications in deforestation detection.

Findings

Seven segmentation methods outperformed the baseline SLIC.

Improved segmentation can enhance citizen science deforestation monitoring.

Analysis guides better method selection for remote sensing image segmentation.

Abstract

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.