# Top-down Green-ups: Satellite Sensing and Deep Models to Predict Buffelgrass Phenology

arXiv: https://arxiv.org/abs/2310.00740 · HTML: https://mlsanity.com/paper/2310.00740/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 35 references that are in the map.
- First posted on arXiv 1 Oct 2023.

## Builds on (most cited first, 5 of 5)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [On Calibration of Modern Neural Networks](https://mlsanity.com/paper/1706.04599/) (2017)
- [A Survey on Neural Network Interpretability](https://mlsanity.com/paper/2012.14261/) (2020)
- [More Diverse Means Better: Multimodal Deep Learning Meets Remote Sensing Imagery Classification](https://mlsanity.com/paper/2008.05457/) (2020)
- [Sensitivity Analysis of Deep Neural Networks](https://mlsanity.com/paper/1901.07152/) (2019)

## Built on by (most cited first, 1 of 1)

- [Fragments to Facts: Partial-Information Fragment Inference from LLMs](https://mlsanity.com/paper/2505.13819/) (2025)

## Abstract (arXiv)

An invasive species of grass known as "buffelgrass" contributes to severe wildfires and biodiversity loss in the Southwest United States. We tackle the problem of predicting buffelgrass "green-ups" (i.e. readiness for herbicidal treatment). To make our predictions, we explore temporal, visual and multi-modal models that combine satellite sensing and deep learning. We find that all of our neural-based approaches improve over conventional buffelgrass green-up models, and discuss how neural model deployment promises significant resource savings.