Multimodal SuperCon: Classifier for Drivers of Deforestation in Indonesia

Bella Septina Ika Hartanti, Valentino Vito, Aniati Murni Arymurthy, Andie Setiyoko
7/29/2022
cs.CVcs.LG

No official code found. Best implementation: bellasih/multimodal_supercon (10 stars, last commit 2023-11-18).

Abstract

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.

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Code Implementations(1)

bellasih/multimodal_superconCommunity100%

Multimodal SuperCon: Classifier for Drivers of Deforestation in Indonesia

104Python, Jupyter NotebookJul 17, 20222 years ago
classificationdeep-learningdeforestation

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Cite this paper

@article{hartanti2022multimodal,
  title  = {Multimodal SuperCon: Classifier for Drivers of Deforestation in Indonesia},
  author = {Bella Septina Ika Hartanti and Valentino Vito and Aniati Murni Arymurthy and Andie Setiyoko},
  year   = {2022},
  eprint = {2207.14656},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2207.14656v1}
}

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