English

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

Computer Vision and Pattern Recognition 2023-10-03 v1 Computers and Society Machine Learning

Abstract

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.

Keywords

Cite

@article{arxiv.2310.00740,
  title  = {Top-down Green-ups: Satellite Sensing and Deep Models to Predict Buffelgrass Phenology},
  author = {Lucas Rosenblatt and Bin Han and Erin Posthumus and Theresa Crimmins and Bill Howe},
  journal= {arXiv preprint arXiv:2310.00740},
  year   = {2023}
}