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

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arxiv 2310.00740 v1 pith:4AUHNRDD submitted 2023-10-01 cs.CV cs.CYcs.LG

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

classification cs.CV cs.CYcs.LG
keywords buffelgrassmodelsdeepgreen-upssatellitesensingapproachesbiodiversity
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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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.

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