Gunjan Barua
Gunjan in the Rocky Mountains of Colorado.
Gunjan Barua is currently a PhD candidate in the Geospatial and Environmental Analysis program at Virginia Tech, where he is establishing himself as a specialist in GeoAI, forest biometry, and operational remote sensing. Scheduled to graduate in Spring 2026, his doctoral research is titled "Advancing Yield Predictions in Pinus taeda (L.): An Artificial Intelligence Approach Leveraging LiDAR-derived Individual Tree Crown Metrics, Competition Indices, and Multispectral Remote Sensing Indices." In this role, Gunjan is engineering reproducible, end-to-end geospatial pipelines that integrate UAV LiDAR and satellite imagery to solve complex forecasting problems. His work goes beyond theory to practical application, developing scalable machine-learning models that support operational forest management and decision-making. As he approaches graduation, Gunjan plans to transition into industry as a forest biometrician, geospatial data scientist, or remote sensing scientist. He is eager to apply his background in predictive modeling and automation to high-impact sectors, specifically looking for opportunities in carbon modeling, ESG reporting, sustainability, and the emerging climate tech landscape.
This advanced technical work is built upon a diverse academic portfolio rooted in spatial problem-solving. Originally from Bangladesh, Gunjan began his journey with a Bachelor’s in Urban and Regional Planning from Khulna University of Engineering & Technology. He subsequently moved to the United States to earn his Master of Science in Geography at Virginia Tech. His master's thesis, "Urban Thermal Map Design Considerations: Color, Shading, and Resolution," focused on the human side of geospatial data, exploring how visualization techniques affect map-reading accuracy and user performance. This combination of computational skills and cartographic design principles makes him a versatile researcher capable of handling data from ingestion to visualization. Gunjan wishes to extend his sincere gratitude to the Forest Productivity Cooperative for their continued support of his doctoral work.
Selected Publication:
Barua, G. et al. (2025). Predicting the yield of Pinus taeda (L.) using UAV LiDAR data in random forest and support vector machine models. Forest Ecology and Management. https://doi.org/10.1016/j.foreco.2025.122977
Gunjan can be reached at gunjanb@vt.edu.
Gunjan presenting his research at the American Geophysical Union (AGU) 2025 Annual Meeting in New Orleans, LA.
