The conservation of Tropical Forests is a key issue due to their importance in the global ecosystem since they contain great biodiversity, carbon storage, rainfall regulation in adjacent regions, and indigenous peoples sheltering.Unfortunately, millions of hectares of tropical forests are lost each year through deforestation and degradation for different and complex reasons.In this research project, a proposal to enhance a novel monitoring system prototype, called ForestEyes, is presented, which aims to combine citizen science and machine learning in detecting deforestation.Citizen Science allows ordinary volunteers to collaborate by inspecting remote sensing image segments over a forest region, seeking to detect deforested or degraded areas, thus participating in a scientific experiment aimed at improving the planet's sustainability, also acquiring more awareness and involvement with the major issue.Machine Learning techniques will be applied in a forest area by using trained databases with samples labeled by volunteers in the previous phase, expanding the coverage for the detection area.Initial experiments over six official campaigns, demonstrated through solid published scientific works, obtained more than 81,000 contributions from 644 different volunteers, allowing to build of a solid training base for classification models, whose results were compared with the official program for monitoring the Brazilian Legal Amazon (PRODES).The methodology developed proved to be promising, both from the point of view of the volunteer's accuracy and the classification techniques applied.This project proposes improvements to the aforementioned project by expanding the application's database and investigating the use of new remote sensing images from multiple optical and radar sensors (SAR), and different regions and periods of time, which implies scientific and technological challenges in the pre-processing, citizen science and machine learning phase.It is also intended to improve the remote sensing image segmentation process, as well as apply super-resolution techniques to the images.