IEEE GRSS Professional Member Spotlight, A Conversation on GeoAI, Remote Sensing, and the Journey Behind KAO
IEEE GRSS Professional Member Spotlight
Abstract
As part of the IEEE Geoscience and Remote Sensing Society (IEEE GRSS) Professional Member Spotlight series, I had the opportunity to discuss my research journey with Madeleine Dawson from the Ocean Remote Sensing Lab at the University of Miami. The interview explored my path from discovering geospatial science at GISTDA to conducting research at Chulalongkorn University and Khon Kaen University, while highlighting the development of Kernel-Adaptive Optimization (KAO), published in IEEE Transactions on Geoscience and Remote Sensing (TGRS). Beyond technical research, the conversation reflected on scientific curiosity, responsible AI, international collaboration, and the importance of building technologies that help us better understand our planet through Earth observation and remote sensing.
A Conversation Beyond Research Papers
It is a tremendous honor to be featured as a Professional Member Spotlight by the IEEE Geoscience and Remote Sensing Society (IEEE GRSS)—one of the world’s leading professional societies dedicated to Earth observation, remote sensing, and geospatial technologies.
The interview was conducted by Madeleine Dawson from the Ocean Remote Sensing Lab, University of Miami, and offered an opportunity to reflect not only on my recent research, but also on the experiences that shaped my journey into GeoAI.
Rather than focusing on a single publication, the conversation explored the people, institutions, and ideas that have influenced my work—from my early days at GISTDA, through postdoctoral research at Chulalongkorn University, to my current role at the College of Computing, Khon Kaen University.
You can read the full interview here: IEEE GRSS Professional Member Spotlight — Dr. Teerapong Panboonyuen
Research Highlight
The interview also discusses my recent work on:
Kernel-Adaptive Optimization (KAO)
published in IEEE Transactions on Geoscience and Remote Sensing (IEEE TGRS).
KAO introduces a new optimization framework designed to improve the stability and efficiency of deep neural network training for remote sensing and Earth observation applications. The interview also reflects on broader themes, including responsible AI, scientific curiosity, and the importance of developing technologies that benefit both society and our understanding of the Earth.
A Meaningful Recognition
For me, this feature represents much more than personal recognition.
It reflects the collective support of mentors, collaborators, students, and institutions that have accompanied my academic journey over the years.
As a researcher from Thailand, I am deeply grateful to contribute to a global scientific community working toward a shared goal: advancing artificial intelligence and remote sensing to better understand and protect our planet.
I sincerely thank IEEE GRSS and Madeleine Dawson for the opportunity to share my story with researchers around the world.
For the Earth, always. 🕊️
Teerapong Panboonyuen
My research focuses on leveraging advanced machine intelligence techniques, specifically computer vision, to enhance semantic understanding, learning representations, visual recognition, and geospatial data interpretation.