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Tag: Deep learning

Machine Learning versus Deep Learning in Land System Science: A Decision-Making Framework for Effective Land Classification

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SOUTHWORTH, SMITH, SAFAEI, RAHAMAN, ALRUZUQ, TEFERA – Machine Learning versus Deep Learning in Land System Science: A Decision-Making Framework for Effective Land Classification Jane Southworth, Audrey C. Smith, Mohammad Safaei, Mashoukur Rahaman, Ali Alruzuq, Bewuket B. Tefera, Carly S. Muir, Hannah V. Herrero Article first published online: 8 APRIL 2024 DOI: https://doi.org/10.3389/frsen.2024.1374862 ABSTRACT: This review […]

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The NASA Lab

The NASA Lab centers around the science of remote sensing and land system science/land change science using Earth observations for interdisciplinary research focused on global environmental change and human-environment interactions. Dr. Southworth’s NASA lab consists of undergraduate, master’s students, doctoral students and post-docs. The lab is run by Dr. Jane Southworth, Professor and Chair of […]

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Hydrological Drought Forecasting Using a Deep Transformer Model

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AMANAMBU, MOSSA, CHEN – Hydrological Drought Forecasting Using a Deep Transformer Model Amobichukwu Amanambu, Joann Mossa, Yin-Hsuen Chen Article first published online: 9 November 2022 DOI: https://doi.org/10.3390/w14223611 ABSTRACT: Hydrological drought forecasting is essential for effective water resource management planning. Innovations in computer science and artificial intelligence (AI) have been incorporated into Earth science research domains […]

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Vision-based movement recognition reveals badminton player footwork using deep learning and binocular positioning

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HU – Vision-based movement recognition reveals badminton player footwork using deep learning and binocular positioning Jiabei Luo, Yujie Hu, Keith Davids, Di Zhang, Cade Gouin, Xiang Li, Xianrui Xu Article first published online: 22 August 2022 DOI: https://doi.org/10.1016/j.heliyon.2022.e10089 ABSTRACT: Coordinating dynamic interceptive actions in sports like badminton requires skilled performance in getting the racket into […]

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