# Effective Data Fusion with Generalized Vegetation Index: Evidence from Land Cover Segmentation in Agriculture

arXiv: https://arxiv.org/abs/2005.03743 · HTML: https://mlsanity.com/paper/2005.03743/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 May 2020.

## Built on by (most cited first, 8 of 8)

- [Learning Neighborhood Representation from Multi-Modal Multi-Graph: Image, Text, Mobility Graph and Beyond](https://mlsanity.com/paper/2105.02489/) (2021)
- [Augmentation Invariance and Adaptive Sampling in Semantic Segmentation of Agricultural Aerial Images](https://mlsanity.com/paper/2204.07969/) (2022)
- [Common Practices and Taxonomy in Deep Multi-view Fusion for Remote Sensing Applications](https://mlsanity.com/paper/2301.01200/) (2022)
- [Surveilling Surveillance: Estimating the Prevalence of Surveillance Cameras with Street View Data](https://mlsanity.com/paper/2105.01764/) (2021)
- [Multimodal SuperCon: Classifier for Drivers of Deforestation in Indonesia](https://mlsanity.com/paper/2207.14656/) (2022)
- [In the Search for Optimal Multi-view Learning Models for Crop Classification with Global Remote Sensing Data](https://mlsanity.com/paper/2403.16582/) (2024)
- [Dual Atrous Separable Convolution for Improving Agricultural Semantic Segmentation](https://mlsanity.com/paper/2506.22570/) (2025)
- [Residue Density Segmentation for Monitoring and Optimizing Tillage Practices](https://mlsanity.com/paper/2102.04866/) (2021)

## Abstract (arXiv)

How can we effectively leverage the domain knowledge from remote sensing to better segment agriculture land cover from satellite images? In this paper, we propose a novel, model-agnostic, data-fusion approach for vegetation-related computer vision tasks. Motivated by the various Vegetation Indices (VIs), which are introduced by domain experts, we systematically reviewed the VIs that are widely used in remote sensing and their feasibility to be incorporated in deep neural networks. To fully leverage the Near-Infrared channel, the traditional Red-Green-Blue channels, and Vegetation Index or its variants, we propose a Generalized Vegetation Index (GVI), a lightweight module that can be easily plugged into many neural network architectures to serve as an additional information input. To smoothly train models with our GVI, we developed an Additive Group Normalization (AGN) module that does not require extra parameters of the prescribed neural networks. Our approach has improved the IoUs of vegetation-related classes by 0.9-1.3 percent and consistently improves the overall mIoU by 2 percent on our baseline.