Scientific projects involving volunteers for analyzing, collecting data, and using their computational resources, known as Citizen Science (CS), have become popular due to advances in information and communication technology (ICT). Many CS projects have been proposed to involve citizens in different knowledge domain such as astronomy, chemistry, mathematics, and physics. This work presents a CS project called ForestEyes, which proposes to track deforestation in rainforests by asking volunteers to analyze and classify remote sensing images. These manually classified data are used as input for training a pattern classifier that will be used to label new remote sensing images. ForestEyes project was created on the Zooniverse.org CS platform, and to attest the quality of the volunteers’ answers, were performed early campaigns with remote sensing images from Brazilian Legal Amazon (BLA). The results were processed and compared to an oracle classification (PRODES – Amazon Deforestation Monitoring Project). Two and a half weeks after launch, more than 35,000 answers from 383 volunteers (117 anonymous and 266 registered users) were received, completing all 2050 tasks. The ForestEyes campaigns’ results have shown that volunteers achieved excellent effectiveness results in remote sensing image classification task. Furthermore, these results show that CS might be a powerful tool to quickly obtain a large amount of high-quality labeled data.