Nattapon Jaroenchai

Professional Summary

Ph.D. student in Geography and Geographic Information Science at UIUC with expertise in geospatial AI, remote sensing, hydrologic feature extraction, and cyberGIS. Developing spatially transferable deep learning models for environmental sustainability and academic contribution. Dedicated to leveraging this experience to foster the growth of CyberGIS understanding, geospatial knowledge, and GeoAI infrastructure within Thailand.

Education

• Ph.D. in Geography & GIScience, University of Illinois Urbana-Champaign, Expected Dec 2025
• M.S. in GIS (PSM), University of Illinois Urbana-Champaign, 2018
• B.Eng. in Computer Engineering, Chulalongkorn University, Thailand, 2011

Research Interests

• Geospatial Artificial Intelligence (GeoAI)
• Hydrologic Mapping & Watershed Analysis
• Remote Sensing & Earth Observation
• Deep Learning and Transfer Learning
• CyberGIS and High-Performance Computing

Thesis

Title: Automating Large-Scale Hydrologic Mapping: A Scalable Machine Learning Framework for Hydrologic Feature Extraction
Advisor: Dr. Shaowen Wang
Abstract: This research develops transfer learning methods to improve spatial generalization of deep learning models for hydrologic feature delineation across physiographic regions using high-resolution remote sensing data.

Publications

• Wang, S., Baig, F., Kang, Y., Li, E., Michels, A., Jaroenchai, N., Padmanabhan, A., Kumar, A., Kalyanam, R., Oller Smith, N. S., Song, X. C., Zhao, L., Manson, S., Ramamurthy, M., Taghavikish, S., & Tarboton, D. (2025). I-GUIDE Platform for Geospatial Data-intensive Convergence Research and Education. PEARC '25, https://doi.org/10.1145/3708035.3736108
  Presents the I-GUIDE cyberinfrastructure platform for reproducible, scalable geospatial research and education.
• Saxton, A., Luo, S., Bode, A., Kramer, W., Zhu, X., Dong, J., Jaroenchai, N., Steffen, C., & Kooper, R. (2024). Accurate Polygonal Feature Extraction from Historical Geologic Maps using Prompted Segmentation. AGU Fall Meeting 2024. https://ui.adsabs.harvard.edu/abs/2024AGUFMSY24A..03S/abstract
  Presented in the Science and Society session at AGU Fall Meeting 2024 (Washington, D.C., Dec 9–13). Discussed polygonal extraction techniques from geologic maps using prompted segmentation methods.
• Luo, S., Saxton, A., Bode, A., Zhu, X., Jaroenchai, N., Dong, J., Kooper, R. (2024). Digitizing Scanned Geologic Maps by Exploiting Few-Shot Models. AGU Fall Meeting 2024. https://ui.adsabs.harvard.edu/abs/2024AGUFMIN31A..02L/abstract
  Presented advanced GeoAI techniques for one-shot segmentation of geologic polygons from scanned maps. The model achieved a median F1 score of 0.91 in the DARPA AI4CMA challenge using prompt-based learning and human-in-the-loop validation.
• Jaroenchai, N., Wang, S., Stanislawski, L. V., Shavers, E., Jiang, Z., Sagan, V., & Usery, E. L. (2024). Transfer learning with convolutional neural networks for hydrological streamline delineation. Environmental Modelling & Software, 181 https://doi.org/10.1016/j.envsoft.2024.106165
  Proposes a CNN-based transfer learning approach for stream mapping that outperforms training-from-scratch models. Cited by 9.
• Vandewalle, R., Michels, A., Li, Z., Jaroenchai, N., & Wang, S. (2024). Building Blocks for Geospatial Software Education Using the I-GUIDE Platform. In I-GUIDE Forum 2024. Purdue University Library Publishing. https://doi.org/10.5703/1288284317803
  Introduces modular software blocks to support learning and rapid development on the I-GUIDE cyberinfrastructure.
• Saxton, A., Dong, J., Bode, A., Jaroenchai, N., Kooper, R., Zhu, X., Kwark, D. H., & Luo, S. (2024). Accurate Feature Extraction from Historical Geologic Maps Using Open-Set Segmentation and Detection. Geosciences, 14(11), 305. https://doi.org/10.3390/geosciences14110305
  Develops open-set segmentation tools for digitizing legacy geologic maps using deep learning and human-in-the-loop methods. Cited by 1.
• Stanislawski, L. V., Jaroenchai, N., Wang, S., Shavers, E. J., Duffy, A., Thiem, P. T., Jiang, Z., Camerer, A. (2023). Transferring Deep Learning Models for Hydrographic Feature Extraction from IfSAR Data in Alaska. GIScience 2023 Workshop on CartoAI: AI for Cartography. https://pubs.usgs.gov/publication/70250212
  Introduces a transfer learning strategy to adapt U-Net for hydrographic delineation across diverse Alaskan terrains. Cited by 1.
• Stanislawski, L. V., Shavers, E. J., Duffy, A. J., Thiem, P., Jaroenchai, N., Wang, S., Jiang, Z., Kronenfeld, B. J., & Buttenfield, B. P. (2022). Scaling-up Deep Learning Predictions of Hydrography from IfSAR Data in Alaska. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLVIII-4/W1-2022, 819–826. https://doi.org/10.5194/isprs-archives-XLVIII-4-W1-2022-819-2022
  Applies deep learning workflows across a large Alaskan dataset and evaluates prediction quality and scalability. Cited by 1.
• Xu, Z., Wang, S., Stanislawski, L. V., Jiang, Z., Jaroenchai, N., Sainju, A. M., Shavers, E. J., Usery, E. L., Chen, L., Li, Z., & Su, B. (2021). An Attention U-Net Model for Detection of Fine-Scale Hydrologic Streamlines. Environmental Modelling & Software, 140, 104992. https://doi.org/10.1016/j.envsoft.2021.104992
  Implements an attention mechanism in U-Net for more accurate stream network predictions from elevation data. Cited by 53.

Conference Presentations

• CEGIS Annual Research Meeting 2025 – Spatially Transferable Hydrographic Feature Delineation
  Presented meta-learning approach for scalable streamline extraction from IfSAR data; emphasized cross-watershed transferability.
• AAG 2025 – Spatially Transferable Hydrographic Feature Delineation
  Presented meta-learning model for stream delineation using IfSAR data; evaluated transferability across watersheds.
• AAG 2023 – Transfer Learning for Streamline Delineation
  Shared performance comparison of pretrained CNNs vs. region-specific training for hydrologic mapping.
• AAG 2022 – Data-Intensive Geospatial Understanding
  Delivered talk on AI-powered watershed extraction and co-chaired deep learning in GIScience panel.
• Geo-Resolution 2022 – Geospatial Perspectives on Climate Change
  Participated in poster session showcasing hydrologic mapping results for climate impact assessment.
• I-GUIDE All-Hands Meeting 2022
  Collaborated with interdisciplinary teams and contributed to spatial AI infrastructure discussions.

Invited Talks & Workshops

• Introducing Transfer Learning: Bridging ImageNet Innovations with Geospatial Analysis on the I-GUIDE Platform
  I-GUIDE Virtual Consulting Office (2025)
  
  Showcased how pre-trained ImageNet models can be fine-tuned for geospatial tasks like hydrologic feature extraction, with hands-on demonstration using the I-GUIDE Jupyter environment.
• Geospatial Transfer Learning on I-GUIDE Platform
  I-GUIDE Virtual Consulting Office (2023)
  
  Gave live demonstration of stream prediction models and their integration into Jupyter workflows.
• Transferring deep learning knowledge for scaling up hydrographic feature extraction
  USGS Hydrography Community Call (2023)
  
  Presented collaborative work on automating stream network extraction with U-Net and IfSAR data.
• Discover the Unknown: What is CyberGIS?
  Department of Statistics, Faculty of Science, Chiang Mai University (2021)
  
  Delivered an invited guest lecture introducing CyberGIS, highlighting the need for high-performance spatial computing to tackle big data, big compute, and global-scale geospatial problems. Included real-world examples from transportation modeling in Bangkok and hydrologic mapping using deep learning.

Assistantships

• Graduate Research Assistant, CyberGIS Center, 2019–Present
  Developed machine learning workflows for hydrologic mapping and contributed to the I-GUIDE cyberinfrastructure platform.
• Web Developer (Graduate Assistant), CyberGIS Center, 2019 – Present
  Designed, developed, and maintained key geospatial web platforms including the CyberGIS Center, I-GUIDE, I-GUIDE Platform (design), and CIGI Lab. Work focused on building user interfaces for data portals and deploying computational GIS services using modern web technologies.

Professional Service & Leadership

• Organizing Committee, Local Arrangement Chair
  I-GUIDE Forum (2025)
  
  Coordinated local logistics and venue arrangements for the I-GUIDE Forum 2025. Led the production of promotional materials to support outreach, visibility, and engagement for the event, contributing to a successful in-person gathering of researchers and practitioners in geospatial AI and convergence science.
• Organizing Committee, Session Organizer & Chair – Geospatial Artificial Intelligence, Remote Sensing, and Deep Learning (Hybrid Session)
  AAG 2025 Symposium on Spatial AI & Data Science for Sustainability (2025)
  
  Served on the organizing committee and chaired a hybrid paper session showcasing innovations in GeoAI and sustainability research. Session Link
• Technical Committee Member, Spatial AI Challenge 2024
  I-GUIDE (2024)
  
  Helped design and evaluate a multi-track international machine learning challenge focused on FAIR data principles, spatial workflows, and AI-ready datasets. Challenge Website
• Organizing Committee, Session Organizer & Chair – Geospatial Artificial Intelligence and Deep Learning (Paper Sessions)
  AAG 2023 Symposium on Harnessing the Geospatial Data Revolution for Sustainability Solutions (2023)
  
  Co-led symposium planning and organized paper sessions on GeoAI applications for climate and environmental sustainability. Session Link
• Representative of International Students, Illinois Spirit & Traditions Council
  University of Illinois Urbana-Champaign (2022)
  
  Advocated for international student voices in a campus-wide council focused on promoting inclusivity, cultural engagement, and the preservation of university traditions.
• Organizing Committee, Session Organizer & Chair – Geospatial Artificial Intelligence and Deep Learning (Paper Sessions)
  AAG 2022 Symposium on Data-Intensive Geospatial Understanding in the Era of AI and CyberGIS (2022)
  
  Collaborated on symposium organization and chaired paper sessions focused on emerging research in geospatial AI, CyberGIS, and data-driven spatial science. Session Link
• Member, Senate Committee on University Student Life
  University of Illinois Urbana-Champaign (2021)
  
  Participated in shared governance by shaping student-focused policies and initiatives, including the proposed fall break, campus expression policy, and student success programs.

Awards & Honors

• Student of the Year – CyberGIS Center & CIGI Lab, UIUC (2020)
• Student of the Year – Professional Science Master’s (GIS concentration), UIUC (2018)
• Leadership Award – Global Leaders Orange Blue Engagement Program, UIUC (2018)