# Multimodal SuperCon: Classifier for Drivers of Deforestation in Indonesia

arXiv: https://arxiv.org/abs/2207.14656 · HTML: https://mlsanity.com/paper/2207.14656/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 33 references that are in the map.
- First posted on arXiv 29 Jul 2022.

## Builds on (most cited first, 17 of 17)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [Supervised Contrastive Learning](https://mlsanity.com/paper/2004.11362/) (2020)
- [Contrastive Multiview Coding](https://mlsanity.com/paper/1906.05849/) (2019)
- [Learning deep representations by mutual information estimation and maximization](https://mlsanity.com/paper/1808.06670/) (2018)
- [Group Equivariant Convolutional Networks](https://mlsanity.com/paper/1602.07576/) (2016)
- [Data-Efficient Image Recognition with Contrastive Predictive Coding](https://mlsanity.com/paper/1905.09272/) (2019)
- [General E(2)-Equivariant Steerable CNNs](https://mlsanity.com/paper/1911.08251/) (2019)
- [Unified Focal loss: Generalising Dice and cross entropy-based losses to handle class imbalanced medical image segmentation](https://mlsanity.com/paper/2102.04525/) (2021)
- [Large-Scale Object Detection in the Wild from Imbalanced Multi-Labels](https://mlsanity.com/paper/2005.08455/) (2020)
- [ForestNet: Classifying Drivers of Deforestation in Indonesia using Deep Learning on Satellite Imagery](https://mlsanity.com/paper/2011.05479/) (2020)
- [Effective Data Fusion with Generalized Vegetation Index: Evidence from Land Cover Segmentation in Agriculture](https://mlsanity.com/paper/2005.03743/) (2020)
- [Rotation Equivariant Deforestation Segmentation and Driver Classification](https://mlsanity.com/paper/2110.13097/) (2021)
- [Paradigm selection for Data Fusion of SAR and Multispectral Sentinel data applied to Land-Cover Classification](https://mlsanity.com/paper/2106.11056/) (2021)
- [SuperCon: Supervised Contrastive Learning for Imbalanced Skin Lesion Classification](https://mlsanity.com/paper/2202.05685/) (2022)

## Built on by (most cited first, 2 of 2)

- [TreeFormers -- An Exploration of Vision Transformers for Deforestation Driver Classification](https://mlsanity.com/paper/2405.15989/) (2024)
- [Forest-Chat: Adapting Vision-Language Agents for Interactive Forest Change Analysis](https://mlsanity.com/paper/2601.14637/) (2026)

## Abstract (arXiv)

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.