Hillson Ghimire
PhD Student, Earth, Environmental and Planetary Sciences, Rice University, Houston, TX
hg77@rice.edu / Website / Google Scholar / GitHub / LinkedIn
Education
PhD in Earth, Environmental and Planetary Sciences
Advisor: Noemi Vergopolan
MS in Geography (GIS Specialization), 4.0/4.0 GPA
Advisor: Maitiniyazi Maimaitijiang
Thesis: AI-assisted Wheat Spike and Spikelet Detection and Density Mapping Using Multiscale Remote Sensing Data to Support Wheat Grain Yield Forecasting
Relevant Coursework: Machine Learning & Deep Learning for Remote Sensing, High Performance Computing, Agricultural & UAS Remote Sensing, Quantitative Remote Sensing, Enterprise Data Modeling
Thesis: AI-assisted Wheat Spike and Spikelet Detection and Density Mapping Using Multiscale Remote Sensing Data to Support Wheat Grain Yield Forecasting
Relevant Coursework: Machine Learning & Deep Learning for Remote Sensing, High Performance Computing, Agricultural & UAS Remote Sensing, Quantitative Remote Sensing, Enterprise Data Modeling
BE in Geomatics Engineering
Thesis: Automated Road Extraction from Sentinel-2 Imagery using a Convolutional Neural Network (with Y. Regmi, A. Chhetri, D. Lamsal, and Z. Kandel)
Relevant Coursework: GIS, Remote Sensing, Photogrammetry, Digital Image Processing, Radar & LiDAR, GPS, Survey Techniques, Python for Geospatial Applications
Relevant Coursework: GIS, Remote Sensing, Photogrammetry, Digital Image Processing, Radar & LiDAR, GPS, Survey Techniques, Python for Geospatial Applications
Research Experience
Doctoral Researcher
- Conducting doctoral research on soil moisture and agricultural drought processes.
- Exploring the integration of satellite observations such as SMAP, land surface models, and machine learning to improve high-resolution soil moisture modeling and prediction.
Graduate Research Assistant
- Developed deep learning frameworks for high-throughput crop phenotyping, automated trait extraction, and disease assessment using UAV and proximal sensing imagery.
- Designed reproducible geospatial and machine learning pipelines integrating multisource imagery, field observations, and crop phenological information.
- Established cloud-supported data annotation and model development workflows to facilitate collaborative research and dataset management.
- Conducted UAV-based field investigations and interdisciplinary research with plant scientists and breeders, contributing to scientific publications and presentations.
Research Associate
- Developed operational satellite-based disaster monitoring and rapid damage assessment workflows using SAR, InSAR, and machine learning, supporting international disaster response through the Sentinel Asia initiative.
- Automated flood extent mapping from JAXA's ALOS-2 SAR imagery, reducing disaster response mapping time from approximately one week to under 24 hours.
- Designed and implemented geospatial information systems and analytical workflows for disaster risk management and environmental monitoring.
- Conducted multidecadal Earth observation analyses of vegetation dynamics, biomass decline, and groundwater depletion to inform regional land restoration planning.
Internships
Geospatial Intern
- Investigated deep learning methods for automated flood extent extraction from SAR imagery to support rapid disaster response.
- Supported the development of geospatial information systems and satellite data processing workflows for disaster risk management.
Geospatial Intern (Remote)
- Developed a deep learning-based road extraction workflow using U-Net and Sentinel-2 imagery to automate infrastructure mapping.
- Applied satellite remote sensing and geospatial analysis to investigate oil spills, extractive activities, and associated environmental impacts across multiple regions.
- Produced geospatial analyses, technical maps, and investigative reports supporting environmental monitoring and public awareness.
Publications
Wheat spike and spikelet detection and counting from high-resolution digital imagery using YOLO with oriented bounding boxes paper / data
H. Ghimire, M. Maimaitijiang, S. Thapa, S. Kaushal, D. J. Koupal, M. M. Billah, K. Poudel, S. Sehgal, M. Singh, S. Subedi, U. U. R. Janjua, and S. K. Sehgal, Scientific Reports, 16, 27355, 2026
M. Maimaitijiang, H. Ghimire, S. Thapa, M. M. Billah, S. Sehgal, M. Singh, S. Kaushal, K. Poudel, S. Subedi, U. U. R. Janjua, L.-O. Makonga, J. Halder, H. S. Gill, M. Sher, J. S. Sidhu, and S. K. Sehgal, arXiv:2601.08863, 2026
Spectral–structural fusion for wheat lodging severity assessment using UAS imagery and deep learning preprint
S. Sehgal, M. Maimaitijiang, J. Halder, K. Poudel, U. U. R. Janjua, H. Ghimire, and S. Subedi, agriRxiv preprint, 2026
AI-assisted wheat spike and spikelet detection and density mapping using multiscale remote sensing data to support wheat grain yield forecasting thesis
H. Ghimire, MS Thesis, South Dakota State University, 2026
Presentations
Dec 2025
Automated wheat spike detection and counting using UAV-based digital imagery and deep learning
Dec 2025
Predicting wheat grain yield and quality using multitemporal and multispectral UAS imagery with transformer models
Nov 2025
Integrating genomics and high-throughput phenotyping with machine learning for predictive breeding in winter wheat abstract
Nov 2025
Advancing wheat disease phenotyping through YOLOv11 and YOLOv12: Automated detection of FHB severity and damaged kernels abstract
Nov 2025
From image to insight: AI-driven wheat monitoring and yield prediction with multi-scale sensing abstract
Apr 2025
Deep learning-assisted wheat yield estimation through spike and spikelet counting from high-resolution imagery
Mar 2025
Deep learning-based detection and counting of wheat spikes and spikelets using high-resolution field imagery for improved yield estimation
Feb 2025
Wheat spike and spikelet detection on close-range digital imagery using deep learning abstract
Nov 2024
Wheat head detection from close-range digital imagery using deep learning photo
Oct 2024
Wheat head detection from close-range digital imagery using deep learning
Dec 2022
The intricacies in the implementation of integrated geospatial platform, database, and application for disaster risk management in Uttarakhand paper
Teaching Experience
Teaching Assistant – Remote Sensing for Disaster Response
- Developed technical course materials, Jupyter notebooks, and hands-on geospatial programming exercises.
- Mentored participants in satellite data analysis, computational workflows, and applied problem-solving.
- Delivered a guest lecture on satellite-based hailstorm damage assessment using PlanetScope imagery, accompanied by reproducible analysis resources.
Data and Software
- Wheat spike and spikelet benchmark dataset (portion of the study dataset, with oriented bounding box annotations), Zenodo, 2026. DOI: 10.5281/zenodo.20215489
- WheatAI, a web-based wheat phenotyping platform (co-developer), 2026.
Honors and Awards
- Ken Kennedy Institute Computational Science and Engineering Recruiting Fellowship ($15,000), Rice University, Aug 2026
- 3rd Place, UAV Track, NDSU AgTech Hackathon, 2026
- Dr. Fritz Gritzner Outstanding Graduate Geography Student Award, South Dakota State University, Jan 2026
- USDA North Central SARE Graduate Student Grant ($19,999) for Wheat-TDM, Oct 2025
Service
- GIS Volunteer, URISA's GISCorps, Jun 2020 – Present. Contributed geospatial data digitization and mapping to NASA's COVID-19 research initiative.
- Session Manager, IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Sep 2020.
Technical Skills
- Programming: Python, SQL, Bash, Git
- Machine Learning: PyTorch, scikit-learn, deep learning, computer vision
- Geospatial: ArcGIS Pro, QGIS, FME, Google Earth Engine, PostGIS, GDAL
- Remote Sensing: SAR and InSAR analysis, multispectral analysis, UAV photogrammetry
- Computing: Linux, high-performance computing, Docker, Google Cloud
- AI Tools: LLMs and agentic coding tools (Claude Code, OpenAI Codex)