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  • Presentation | H43A: AI/ML and Remote Sensing for Water and Wetlands: Integrating Spatial Analytics and Participatory Approaches for Climate-Resilient Ecosystem Management II Oral
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  • H43A-03: Leveraging Machine Learning for Coastal Freshwater Floodplain Wetland Identification and Habitat Suitability
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  • Location Icon217-219
    NOLA CC
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Author(s):
Elliott White Jr., Stanford University (First Author, Presenting Author)
Layla Tadjpour, Collaborative Earth
Margaux Masson-Forsythe, Surgical Data Science Collective
Nikhil Raj DEEP, University of Florida
Aaron Hirsh, Collaborative Earth


Swamps (forested wetlands) are common across the Southeastern United States, however they are threatened by climate change and human driven effects to the landscape. Our ability to assess this ecosystem at large scale is limited due to the lack of knowledge about where the ecosystem currently and can exist in the future. We used publicly available data and advances in machine learning to produce the first maps that focus specifically on this ecosystem. These products are now being used to identify locations for swamp restoration and conservation with the goal of generating carbon credits for landowners.



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