Prajwol Babu Subedi
This is a likely match — the affiliation was inferred from OpenAlex, ORCID, and web sources but has not been fully confirmed. Treat with appropriate caution.
Researcher
Also affiliated: Pokhara University (2022); Tribhuvan University (2021–2022)
Faculty Researcher
Research Areas
Links
Biography and Research Information
OverviewAI-generated summary
Prajwol Babu Subedi's research focuses on the application of remote sensing and Geographic Information System (GIS) technologies for environmental assessment, particularly within forest ecosystems. His work investigates vegetation structure, carbon stock potential, and the dynamics of deforestation and forest degradation. Subedi has published studies on estimating above-ground forest biomass using high-resolution imagery, machine learning, and deep learning techniques. He also conducts research on mapping land use land cover changes and identifying forest fire risk zones. His collaborators include Hamdi A. Zurqani and Marco A. Yáñez from the University of Arkansas at Monticello, and Mahamad Sayab Miya from the University of Arkansas at Fayetteville.
Metrics
- h-index: 3
- Publications: 11
- Citations: 23
Selected Publications
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Multi-sensor forest aboveground biomass estimation using GEDI, machine learning, and deep learning techniques (2025)
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Estimating Above Ground Forest Biomass Using High-Resolution NAIP Imagery and Deep Learning (2025)
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Comparison of Supervised Machine Learning Algorithms for Extracting Tree Canopy Cover using High-Resolution Imagery and Google Earth Engine (2025)
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Automated Individual Tree Crown Detection and Segmentation using Simple Non-Iterative Clustering (SNIC) Algorithms and High-Resolution LiDAR (2025)
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Estimating Above Ground Forest Biomass Using High-Resolution NAIP Imagery, Machine Learning, and Google Earth Engine (2024)
Collaboration Network
Top Collaborators
- Estimating Above Ground Forest Biomass Using High-Resolution NAIP Imagery, Machine Learning, and Google Earth Engine
- Multi-sensor forest aboveground biomass estimation using GEDI, machine learning, and deep learning techniques
- Automated Individual Tree Crown Detection and Segmentation using Simple Non-Iterative Clustering (SNIC) Algorithms and High-Resolution LiDAR
- Comparison of Supervised Machine Learning Algorithms for Extracting Tree Canopy Cover using High-Resolution Imagery and Google Earth Engine
- Estimating Above Ground Forest Biomass Using High-Resolution NAIP Imagery and Deep Learning
- Automated Individual Tree Crown Detection and Segmentation using Simple Non-Iterative Clustering (SNIC) Algorithms and High-Resolution LiDAR
- Comparison of Supervised Machine Learning Algorithms for Extracting Tree Canopy Cover using High-Resolution Imagery and Google Earth Engine
- Automated Individual Tree Crown Detection and Segmentation using Simple Non-Iterative Clustering (SNIC) Algorithms and High-Resolution LiDAR
- Comparison of Supervised Machine Learning Algorithms for Extracting Tree Canopy Cover using High-Resolution Imagery and Google Earth Engine
- Automated Individual Tree Crown Detection and Segmentation using Simple Non-Iterative Clustering (SNIC) Algorithms and High-Resolution LiDAR
- Comparison of Supervised Machine Learning Algorithms for Extracting Tree Canopy Cover using High-Resolution Imagery and Google Earth Engine
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