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.

翻译：一种名为“布法尔草”的入侵草种在美国西南部加剧了严重野火和生物多样性丧失。我们致力于解决预测布法尔草“绿化期”（即适宜进行除草剂处理的时机）的问题。为进行预测，我们探索了结合卫星遥感与深度学习的时序、视觉及多模态模型。研究发现，所有基于神经网络的方法均优于传统的布法尔草绿化期模型，并探讨了神经网络模型部署在节约资源方面的显著潜力。