论文标题
多模式超级频道：印度尼西亚森林砍伐的分类器
Multimodal SuperCon: Classifier for Drivers of Deforestation in Indonesia
论文作者
论文摘要
森林砍伐是导致气候变化的因素之一。气候变化对人类的生命有严重的影响，并且由于温室气体（例如二氧化碳）的排放到大气中而发生。重要的是要了解缓解工作的森林砍伐原因，但是缺乏数据驱动的研究来预测这些森林砍伐驱动因素。在这项工作中，我们建议使用从Landsat 8获得的卫星图像对印度尼西亚森林砍伐的驾驶员进行对比的学习体系结构。多模式SuperCon是一种结合对比度学习和多模式融合以处理可用的Deforestation数据集的架构。我们提出的模型优于先前的驾驶员分类工作，与同一任务的最先进的旋转模型相比，准确性提高了7％。
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.
