{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/top-down-green-ups-satellite-sensing-and-deep","title":"Top-down Green-ups: Satellite Sensing and Deep Models to Predict Buffelgrass Phenology","arxiv_id":"2310.00740","date":"2023-10-01","proceeding":null,"authors":["Lucas Rosenblatt","Bin Han","Erin Posthumus","Theresa Crimmins","Bill Howe"],"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.","url_abs":"https://arxiv.org/abs/2310.00740v1","url_pdf":"https://arxiv.org/pdf/2310.00740v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"top-down-green-ups-satellite-sensing-and-deep","repo_url":"https://github.com/lurosenb/phenology_projects","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}