Barira Rashid
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Also affiliated: National University of Sciences and Technology (2018–2020)
Research Areas
Biomedical Subjects
Links
Biography and Research Information
OverviewAI-generated summary
Barira Rashid's research focuses on the application of machine learning techniques for environmental monitoring and resource management, particularly concerning animal feeding operations and agricultural landscapes in the United States. Her work addresses gaps in existing livestock data, which are identified as a barrier to effective environmental and disease management. Rashid also investigates the structural and functional connectivity of thermal refuges for desert wildlife, examining the impacts of climate change and urbanization. Her publications explore methods for mapping livestock infrastructure and identifying animal feeding operations at a parcel scale using object-based image analysis and machine learning. Rashid has collaborated with researchers at the University of Arkansas at Fayetteville, including Rebecca Logsdon Muenich, Arghajeet Saha, Ting Liu, and Jada M. Thompson, on multiple shared publications.
Metrics
- h-index: 4
- Publications: 8
- Citations: 65
Selected Publications
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Beyond linearity: reimagining AI as a participant in circular bioeconomies (2026)
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Structural and Functional Connectivity of Thermal Refuges in a Desert City: Impacts of Climate Change and Urbanization on Desert Wildlife (2025)
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Gaps in U.S. livestock data are a barrier to effective environmental and disease management (2025)
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Machine learning-based identification of animal feeding operations in the United States on a parcel-scale (2025)
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Navigating Earth Sciences: Addressing Equity and Sustainability Through Interdisciplinary Endeavors (2024)
Collaboration Network
Top Collaborators
- Gaps in U.S. livestock data are a barrier to effective environmental and disease management
- Machine learning-based identification of animal feeding operations in the United States on a parcel-scale
- Gaps in U.S. livestock data are a barrier to effective environmental and disease management
- Machine learning-based identification of animal feeding operations in the United States on a parcel-scale
- Machine learning-based identification of animal feeding operations in the United States on a parcel-scale
- Machine learning-based identification of animal feeding operations in the United States on a parcel-scale
- Gaps in U.S. livestock data are a barrier to effective environmental and disease management
- Gaps in U.S. livestock data are a barrier to effective environmental and disease management
- Gaps in U.S. livestock data are a barrier to effective environmental and disease management
- Gaps in U.S. livestock data are a barrier to effective environmental and disease management
- Gaps in U.S. livestock data are a barrier to effective environmental and disease management
- Gaps in U.S. livestock data are a barrier to effective environmental and disease management
- Gaps in U.S. livestock data are a barrier to effective environmental and disease management
- Gaps in U.S. livestock data are a barrier to effective environmental and disease management
- Gaps in U.S. livestock data are a barrier to effective environmental and disease management
- Gaps in U.S. livestock data are a barrier to effective environmental and disease management
- Gaps in U.S. livestock data are a barrier to effective environmental and disease management
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