The Use of Artificial Intelligence to Curb Deforestation in the Brazilian Rainforest: Methods, Infrastructure, and Implications

Abstract

Tropical rainforests like the Amazon are invaluable ecosystems for human society and biodiversity. However, they are facing unprecedented threats, primarily from deforestation. This chapter explores the use of machine learning (ML) and deep learning (DL) to address this pressing environmental problem. By analyzing different ML/DL methods, we show how these tools can be used to understand deforestation patterns in the Brazilian Amazon better. Specifically, we discuss how ML/DL can help identify the drivers of deforestation, improve remote sensing-based monitoring, and predict future deforestation trends. Our results, particularly the role of ML/DL in providing actionable insights, empower decision-makers and policymakers with the knowledge to make informed choices. Ultimately, these strategies contribute to more effective forest conservation measures and sustainable land use, reassuring the audience about the reliability of our research.