Professional Member Spotlight: Dr. Teerapong Panboonyuen

Dr. Teerapong Panboonyuen (also known as Dr. Kao Panboonyuen) is an Adjunct Professor in Spatiotemporal AI at the College of Computing, Khon Kaen University, concurrently serving as a Senior Research Scientist and Director of the PBY Artificial Intelligence Laboratory (PBYAIL)—an institute dedicated to ethical AI research at a global scale, leveraging Earth-observation data and responsible AI to protect our planet. He also serves as a Postdoctoral Researcher in the Department of Survey Engineering, Faculty of Engineering at Chulalongkorn University, the same institution where he earned his Ph.D. in Computer Engineering. With great honor, Dr. Panboonyuen will soon join the Government Savings Bank (GSB) as Deputy Director of AI, aiming to lead strategic AI innovation and advance trustworthy, scalable, and interpretable systems for Thailand’s national banking ecosystem. As a prominent voice in deep learning for Earth observation, his research spans advanced remote sensing applications to generative architectures. Most notably, he is the pioneer behind ‘KAO’—a breakthrough framework introducing Kernel-Adaptive Optimization and Latent Space Conditioning to diffusion models for high-fidelity satellite image inpainting, published in the prestigious journal IEEE Transactions on Geoscience and Remote Sensing (TGRS). Beyond his academic and corporate milestones, he actively contributes back to the engineering community as an expert reviewer for several leading IEEE publications.

Background:

I am Teerapong Panboonyuen, but everyone in the community—and across open-source platforms like GitHub and Hugging Face—knows me as Kao Panboonyuen.

Currently, my professional work spans three core pillars. First, I am an Adjunct Professor in Spatiotemporal AI at the College of Computing, Khon Kaen University, where I teach AI fundamentals to undergraduate students. Second, I am a Postdoctoral Researcher within the Department of Survey Engineering, Faculty of Engineering at Chulalongkorn University. Lastly, I direct the PBY Artificial Intelligence Laboratory (PBYAIL), an advanced research institute in Thailand dedicated to ethical AI research at a global scale—leveraging Earth-observation data and responsible AI to protect our planet. My foundational background is in Computer Engineering, and my Ph.D. specifically focused on GeoAI, which served as the exact launching pad where I discovered my lifelong passion for utilizing deep learning to analyze satellite imagery.

My current research focuses on Generative AI for satellite image restoration, particularly in reconstructing missing or occluded areas caused by cloud cover or sensor degradation. My most recent breakthrough is KAO (Kernel-Adaptive Optimization), a diffusion-based framework explicitly architected for satellite image inpainting. Unlike conventional models that require extensive retraining or suffer from heavy computational overhead during inference, KAO introduces a Latent Space Conditioning approach. By optimizing directly within a compact latent space and incorporating Explicit Propagation, it achieves unprecedented efficiency, stability, and structural precision. I am incredibly proud that this framework was recently published in IEEE Transactions on Geoscience and Remote Sensing (TGRS).

My journey in remote sensing actually began with Land Use and Land Cover (LULC) classification during the early days of my doctoral studies. At that time, I had the privilege of collaborating with GISTDA (Geo-Informatics and Space Technology Development Agency). They were truly my ‘first school’—the foundational place that introduced me to the beautiful world of geoscience and remote sensing, and I remain deeply grateful to them. Together, we trained deep learning models to map agricultural land cover for vital crops like sugarcane and pineapple.

More importantly, that invaluable experience solidified my ultimate mission: to utilize Spatiotemporal AI to support my country, especially in addressing pressing regional and global challenges like PM 2.5 air pollution, where AI can directly improve people’s everyday lives. Along my journey, I have explored various AI applications, including medical imaging, but remote sensing is where I feel the most fulfilled. It is the field that truly defines who I am and brings me the greatest happiness.

How long have you been a member? Tell us about your GRSS journey.

I have been a member of GRSS for about two years now. My journey truly kicked off through my active engagement with IEEE GRSS conferences and publishing in their journals. I am incredibly excited to be attending IGARSS this year in Washington, D.C.

I deeply value this entire experience because it allows me to stay seamlessly connected with rapid advancements, cutting-edge resources, and to actively contribute back to our global community. This year in particular, I’ve noticed a massive, defining shift toward integrating Large Language Models (LLMs) and multi-modal AI into satellite imagery workflows.

Seeing so many papers at IGARSS adopting these advanced techniques is phenomenal. It proves how fast our field is evolving, and I highly encourage everyone to closely follow the groundbreaking research being presented in Washington, D.C. this year!

What inspired you to join GRSS?

I joined GRSS because I wanted to actively help expand the horizons of the remote sensing field. Coming from a foundational background in AI and Computer Engineering, I saw a clear, meaningful opportunity to contribute from a deeply technical computer science perspective.

Serving as a reviewer for GRSS publications has been incredibly eye-opening; it allows me to witness firsthand how the field is evolving in real time. AI brings entirely new, innovative paradigms to solving traditional remote sensing challenges. This exposure as a reviewer gives me a panoramic view of the community’s direction—where the field is heading and what critical problems are becoming the next core priorities.

Ultimately, it helps me bring those invaluable global insights, such as emerging methodologies and novel tasks, back home to adapt and deploy them for real-world applications right here in Thailand.

How has GRSS contributed to your professional growth?

GRSS has been an absolute catalyst for my professional and academic growth. Actively following the groundbreaking research generated by this community—especially the high-impact papers published in IEEE TGRS—continuously feeds me with innovative techniques and pioneering ideas.

This deep engagement gives me a crystal-clear understanding of how our global community is evolving, from the rise of emerging algorithms like vision-language models and LLMs to shifting paradigms in earth observation. GRSS helps me seamlessly translate these overarching global trends into tangible, tactical applications tailored to my home country. I’ve found that even when certain machine learning methods are established in mainstream computer science, adapting them to the unique, multi-spectral complexities of satellite data always unlocks profound new insights.

This journey of continual learning has immensely broadened my perspective. It ultimately inspired and enabled me to contribute back by publishing my own framework, KAO, in TGRS last year. It is a deeply rewarding, symbiotic relationship where GRSS fuels my growth, and in turn, allows me to produce research that is both meaningful to the scientific community and highly impactful for real-world society.

What advice would you give to individuals considering joining the GRSS community?

I would whole-heartedly encourage them to take that leap! For young professionals and students especially, my biggest advice is to get involved as early as possible.

You don’t need to worry about having a fully-formed abstract or a major paper ready to publish right away. There is immense, irreplaceable value in simply stepping into the room—attending the conferences, participating in workshops, and actively volunteering. GRSS is a unique global crossroads that gathers top-tier researchers and brilliant minds from every corner of the world.

Exposing yourself to this rich diversity of perspectives and cutting-edge ideas will naturally shape, refine, and elevate your own research direction. It takes away the pressure and replaces it with inspiration, which ultimately flows into producing deeply meaningful, high-impact work down the road.

Why do you think it is important to create a chapter in your region?

Establishing a local chapter is absolutely vital. I closely collaborate with Thailand’s national space agency, GISTDA, and my overarching mission is to meaningfully contribute to the strategic development of remote sensing across the nation.

Bringing global benchmarks like GRSS to our local ecosystem is a crucial step in that direction. While there are immensely strong, established research communities in Europe and the United States, it is equally imperative that we build and nurture robust, self-sustaining capabilities within our own region. GRSS plays an indispensable role in bridging us to that global stage, ensuring our local talent stays synchronized with the frontier of space technology.

Of course, pioneering a local chapter comes with its own set of unique challenges and rewards. People lead incredibly busy professional lives, so community participation naturally fluctuates. To address this, I am fully committed to cultivating a sustainable, highly active community by establishing consistent technical activities, organizing knowledge-sharing sessions, and actively bridging the gap between academia and industrial sectors. Ultimately, I want this chapter to serve as a driving force that empowers the next generation of remote sensing professionals in Thailand.

What has been something in your chapter that you are very proud of?

One thing I am particularly proud of is seeing my research translate into tangible utility for others. Since its publication, my recent work on KAO has garnered substantial interest from both independent researchers and prominent organizations worldwide.

I’ve received numerous inquiries and messages from peers who are eager to implement and build upon my framework in their own projects. This validation is incredibly rewarding because I am a staunch believer in the power of open-source science and democratic knowledge sharing. I consistently champion this cause because I believe that true innovation happens when we open our doors, share our methodologies, and collaborate globally to push the boundaries of what AI and remote sensing can achieve.

What is the vision of the future of your chapter?

Looking ahead, our fundamental vision is to continuously develop and democratize high-impact, open-source frameworks in Spatiotemporal AI and remote sensing.

I firmly believe that Geospatial Artificial Intelligence possesses transformative potential to mitigate some of the world’s most critical global challenges, particularly severe natural disasters such as earthquakes, tsunamis, and landslides. These are catastrophic events that directly and profoundly impact human lives, and our chapter wants to be at the forefront of building technological resilience against them.

Crucially, our vision is built on the philosophy that AI is not here to replace human expertise. Instead, we see it as an incredibly powerful, intelligent co-pilot. By positioning AI as a sophisticated assistant in remote sensing workflows, we can empower researchers, disaster response teams, and policymakers to make faster, data-driven decisions that ultimately save lives and protect communities worldwide. Ultimately, my enduring aspiration is to champion and pioneer Geospatial AI in Thailand, channeling the full force of advanced AI to elevate remote sensing workflows for national development, while contributing meaningful solutions that protect our planet and forge a better, more resilient global future for all.

You can connect with Dr. Kao and explore his work through the following platforms:

• Personal Website: kaopanboonyuen.github.io/ — Explore his personal milestones, publications, and academic journey.
• GitHub Profile: github.com/kaopanboonyuen — Access his open-source codebases for spatiotemporal AI, generative modeling, and remote sensing applications.
• Research Laboratory (PBYAIL): pbylab.github.io/ — Stay updated on the latest ethical AI initiatives and Earth-observation research.