Bidhyananda Yadav, PhD
Research Associate · Geocomputational Scientist · Spatial Data Engineer
Byrd Polar and Climate Research Center, The Ohio State University
yadav.111@osu.edu · Google Scholar
About
I build systems that turn satellite data into scientific knowledge. Over seven years at OSU's Byrd Polar and Climate Research Center, I have designed and deployed large-scale geospatial pipelines, machine learning algorithms, and analytics platforms for NASA and NSF Earth observation missions — working across the full stack from raw HPC data ingestion to stakeholder-facing dashboards.
My background sits at the intersection of remote sensing physics, computational hydrology, and applied ML. I trained as a geoscientist (PhD, UF) — my dissertation (2018) applied eight ML algorithms (Random Forest, Gradient Boosting, Extra Trees, SVM, regularized regression) to predict hydrologic signatures across 1,200+ USGS gages at continental scale, with rigorous cross-validation, feature selection, and ensemble evaluation. That grounding in applied statistical ML preceded my OSU role and shaped how I approach model development: feature engineering first, proper evaluation discipline, production-aware pipelines. Since joining OSU, that foundation extended into deep learning integration, large-scale satellite data workflows, and AI application delivery for NASA and NSF programmes.
My work bridges domain science and engineering — translating scientific questions into production-grade data systems, AI workflows, and analytics platforms. I approach complex problems by combining deep methodology discipline with AI-assisted development (Copilot, Claude, HuggingFace), enabling rapid implementation without sacrificing scientific rigor.
Selected Work
SWOT Global River Dashboard
NASA's Surface Water and Ocean Topography satellite generates continuous global river and lake observations. I co-led the remote sensing workflows for river discharge and lake dynamics estimation, then built the worldwide visual analytics dashboard (swotviz.cuahsi.io) for federal agency partners and the research community. The platform handles multi-terabyte SWOT swath data and serves interactive visualizations to NOAA, USGS, and NASA stakeholders.
Continental Snow Water Equivalent Dataset
There is no direct satellite sensor for snow water equivalent at the scale and resolution needed for water resource decision-making. I fused outputs from NASA's Land Information System (LIS) land surface model with MODIS and VIIRS remote sensing observations to produce a 20-year (2000-2020), daily, 0.01° resolution SWE dataset covering all of North America. The pipeline runs on OSU's HPC cluster in Julia and Python, processing roughly 30 TB of input data into a reproducible, publication-quality data product.
Deep Learning for Water Body Classification
Sub-meter detection of river surface water from satellite imagery requires models that go well beyond standard thresholding approaches. I contributed the remote-sensing data engineering and workflow-integration layers for PyTorch-based water classification research, working alongside deep-learning model architects. My work centered on satellite-data acquisition (Sentinel-2, Maxar/WorldView), preprocessing, ArcticDEM-derived 3D context extraction, model-output integration, and downstream hydrology pipeline operationalization. The collaboration produced two peer-reviewed publications.
ICESat-2 Processing Toolkit
Processing ICESat-2 laser altimetry data across Antarctica and the Arctic involves correcting systematic vertical biases against reference DEMs (ArcticDEM, REMA, WorldDEM) at continental scale. I built an open-source Python toolkit that automated what had been weeks-long manual processing workflows into a reproducible pipeline, presented at NASA Goddard and adopted by collaborators across the cryosphere community.
Greenland Glacier Velocity
Outlet glaciers carry Greenland's ice to the ocean, and tracking how their speed changes needs denser imagery than any one satellite provides. I built a satellite pipeline that maps ice velocity across Greenland's outlet glaciers from Sentinel-2 (A/B/C) and Landsat imagery. The pipeline is open source, its data products are archived at NSIDC DAAC, and the work is published in The Cryosphere.
Fluid Earth Viewer
Global atmospheric and ocean model output is hard to read for anyone outside the field. On this NSF-funded project I implemented the climate metric aggregations (monthly, seasonal, annual) and derived data products behind Fluid Earth Viewer, an interactive web application for public science communication. The platform was later adopted by the Permafrost Discovery Gateway.
Experience
| Role | Organization | Years |
|---|---|---|
| Research Associate (GIS/Remote Sensing) | The Ohio State University, Byrd Polar and Climate Research Center | 2019 – Present |
| Graduate Research Assistant | University of Florida, Civil Engineering | 2010 – 2019 |
| Research Assistant | National Center for Airborne Laser Mapping (NCALM), UF | 2006 – 2009 |
Education
| Degree | Institution | Year |
|---|---|---|
| PhD, Civil Engineering | University of Florida | 2018 |
| MS, Civil Engineering (Geosensing Systems Engineering) | University of Florida | 2009 |
| MSc, Geo-Information Science | Wageningen University, Netherlands | 2004 |
| BSc, Environmental Science | Kathmandu University, Nepal | 2001 |
Peer-Reviewed Publications
- Shutkin, T.Y. et al. (2025). Modeling the impacts of climate trends and lake formation on the retreat of a tropical Andean glacier (1962-2020). The Cryosphere, 19, 4835–4853.
- Andreadis, K.M. et al. (2025). A first look at river discharge estimation from SWOT satellite observations. Geophysical Research Letters, 52, e2024GL114185.
- Durand, M., Dai, C., Moortgat, J., Yadav, B. et al. (2024). Using river hypsometry to improve remote sensing of river discharge. Remote Sensing of Environment, 315, 114455.
- Ziwei, L., Leong, W.J., Durand, M., Howat, I., Wadkowski, K., Yadav, B., Moortgat, J. (2023). Super-resolution deep neural networks for water classification from free multispectral satellite imagery. Journal of Hydrology, 626, 130248.
- Moortgat, J., Ziwei, L., Durand, M., Howat, I., Yadav, B. (2022). Deep learning models for river classification at sub-meter resolutions from multispectral and panchromatic commercial satellite imagery. Remote Sensing of Environment, 282, 113279.
- Chudley, T.R., Howat, I.M., Yadav, B., and Noh, M.J. (2022). Empirical correction of systematic orthorectification error in Sentinel-2 velocity fields for Greenlandic outlet glaciers. The Cryosphere, 16, 2629–2642.
- Yadav, B. and Hatfield, K. (2018). Stream network conflation with topographic DEMs. Environmental Modelling & Software, 102, 241–249.
Research Funding
| Grant | Agency | Role | Period | Amount |
|---|---|---|---|---|
| Adopting SWOT measurements to improve decision making for currently ungauged basins: Decision support for Alaska | NASA | Co-Investigator | 2022–2024 | $925,353 |
| The Permafrost Discovery Gateway: Navigating the New Arctic through Big Imagery, AI, and cyberinfrastructure | NSF | Collaborator | 2019–2024 | $106,230 |
Service
- Proposal reviewer, NASA ROSES grants
- Manuscript reviewer: International Journal of Digital Earth, Journal of Mountain Science, NSF EarthCube Notebooks
- OSU Geospatial Steering Committee