Multimodal SuperCon: Classifier for Drivers of Deforestation in Indonesia

• Main Authors: Hartanti, Bella Septina Ika, Vito, Valentino, Arymurthy, Aniati Murni, Setiyoko, Andie
• Format: Journal Article
• Language: English
• Published: 29-07-2022
• Subjects: Computer Science - Computer Vision and Pattern Recognition; Computer Science - Learning
• DOI: 10.48550/arxiv.2207.14656

Deforestation is one of the contributing factors to climate change. Climate
change has a serious impact on human life, and it occurs due to emission of
greenhouse gases, such as carbon dioxide, to the atmosphere. It is important to
know the causes of deforestation for mitigation efforts, but there is a lack of
data-driven research studies to predict these deforestation drivers. In this
work, we propose a contrastive learning architecture, called Multimodal
SuperCon, for classifying drivers of deforestation in Indonesia using satellite
images obtained from Landsat 8. Multimodal SuperCon is an architecture which
combines contrastive learning and multimodal fusion to handle the available
deforestation dataset. Our proposed model outperforms previous work on driver
classification, giving a 7% improvement in accuracy in comparison to a
state-of-the-art rotation equivariant model for the same task.