ForestEyes Project: Conception, Enhancements, and Challenges

TL;DR

The ForestEyes project leverages citizen science volunteers to analyze remote sensing images for deforestation monitoring in rainforests, demonstrating scalable data collection and promising accuracy compared to official groundtruth data.

Contribution

This work introduces the ForestEyes citizen science platform for rainforest deforestation monitoring and evaluates its effectiveness against official data sources.

Findings

Over 86,000 volunteer answers collected in short timeframes.

High correlation between citizen science data and official deforestation data.

Effective workflows for remote sensing image analysis using volunteer input.

Abstract

Rainforests play an important role in the global ecosystem. However, significant regions of them are facing deforestation and degradation due to several reasons. Diverse government and private initiatives were created to monitor and alert for deforestation increases from remote sensing images, using different ways to deal with the notable amount of generated data. Citizen Science projects can also be used to reach the same goal. Citizen Science consists of scientific research involving nonprofessional volunteers for analyzing, collecting data, and using their computational resources to outcome advancements in science and to increase the public's understanding of problems in specific knowledge areas such as astronomy, chemistry, mathematics, and physics. In this sense, this work presents a Citizen Science project called ForestEyes, which uses volunteer's answers through the analysis and classification of remote sensing images to monitor deforestation regions in rainforests. To evaluate the quality of those answers, different campaigns/workflows were launched using remote sensing images from Brazilian Legal Amazon and their results were compared to an official groundtruth from the Amazon Deforestation Monitoring Project PRODES. In this work, the first two workflows that enclose the State of Rond\^onia in the years 2013 and 2016 received more than $35,000$ answers from $383$ volunteers in the $2,050$ created tasks in only two and a half weeks after their launch. For the other four workflows, even enclosing the same area (Rond\^onia) and different setups (e.g., image segmentation method, image resolution, and detection target), they received $51,035$ volunteers' answers gathered from $281$ volunteers in $3,358$ tasks. In the performed experiments...