Evaluation of Machine-Learning Architectures and Expanded Feature Sets for GEDI Footprint Aboveground Biomass Density Prediction
Evaluation of Machine-Learning Architectures and Expanded Feature Sets for GEDI Footprint Aboveground Biomass Density Prediction
Eugene Seo1, John David Armston2, Laura Duncanson3, James Bryan Blair4, Jamis Bruening5, Tiago de Conto5, Ralph Dubayah6, Steven Hancock7, Sean Healey8, Michelle A Hofton5, Scott B Luthcke9, David Minor3, Paul L Patterson10, Hao Tang11, Matthew Harrison1 and James R Kellner12, (1)Brown University, Applied Math, Providence, United States, (2)University of Maryland College Park, College Park, United States, (3)University of Maryland College Park, Geographical Sciences, College Park, United States, (4)NASA Goddard Space Flight Center, Code 61A, Greenbelt, MD, United States, (5)University of Maryland College Park, Geographical Sciences, College Park, MD, United States, (6)University of Maryland College Park, Department of Geographical Sciences, College Park, United States, (7)University of Edinburgh, School of Geosciences, Edinburgh, United Kingdom, (8)Rocky Mountain Research Station, Ogden, UT, United States, (9)NASA Goddard Space Flight Center, Geodesy and Geophysics Lab, Greenbelt, MD, United States, (10)Rocky Mountain Research Station, Ogden, United States, (11)National University of Singapore, Geography, Singapore, Singapore, (12)Brown University, Providence, RI, United States
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