Dear Christos, Alexis, and Simon, My name is Nikitas Rafail Karachalios. I am a 4th-year undergraduate student at the Department of Informatics and Telecommunications, National and Kapodistrian University of Athens, specializing in Machine Learning and Deep Learning. I am writing about the "Open-Source AI Framework for Thermal Satellite Payload Data Analysis" GSoC 2026 project, as I would love to contribute to it. My most relevant work is a comparative study of deep learning methods for satellite image classification on EuroSAT ( https://github.com/NikitasKrh/Modern-Deep-Learning-Methods-A-Comparative-Study-on-Satellite-Data ). It covers multiple learning paradigms (supervised, self-supervised, few-shot, zero-shot, parameter-efficient fine-tuning), all implemented end-to-end in PyTorch. The focus was not just on training and comparing models, but on understanding why they behave the way they do. Each experiment is followed by a detailed Q&A section that analyzes the results, explains failure cases, and connects findings across methods, such as linking representation-level analysis (t-SNE, cosine similarities) back to classification errors. This kind of analysis is something I genuinely enjoy and want to develop further. I have been going through the TIRAuxCloud codebase carefully, tracing the data pipeline (multi-band GeoTIFF loading, auxiliary meteorological channels stacked alongside thermal bands, per-band normalization) and the different segmentation architectures the project supports. What stood out to me is that adding auxiliary meteorological data consistently improves cloud detection over using thermal bands alone, since it shows that context beyond the raw spectral signal really matters. The whole approach of working in thermal infrared instead of optical (night-time capability, no snow-cloud confusion) was new to me and sparked my interest. I also looked at some of Orion-AI-Lab's other projects, KuroSiwo for SAR-based flood mapping, Sen4AgriNet for Sentinel-2 crop segmentation, which gave me a better sense of the lab's overall work in remote sensing and helped me think about the questions below. My background is in image classification, which I see as a solid foundation for moving toward segmentation, since many of the core concepts (encoder architectures, transfer learning, loss functions, evaluation under different data regimes) carry over directly. I am actively building on this by studying segmentation methods and learning to work with geospatial data formats. I have a couple of questions that would really help me write a focused proposal: 1. Scope - cloud detection vs. new thermal tasks. The project description mentions building a general-purpose framework that covers dataset creation, model training, and advanced analysis tools like uncertainty quantification and explainability, with application scenarios ranging from cloud detection to thermal event monitoring. Since TIRAuxCloud already has a solid cloud detection pipeline in place, I would appreciate some guidance on how you envision the GSoC work building on top of it. Should the main focus be on generalizing and improving what already exists (for example supporting additional sensors beyond Landsat TIRS and VIIRS, or adding uncertainty estimation), or is there also an expectation to start supporting new thermal analysis tasks during the project? 2. Is there a Discord server, or any other communication channel where contributors can discuss the project? I would love to stay in the loop and engage with the community. I could follow up with a detailed draft proposal once I have your input. In the meantime, if there is a starter task I could pick up or a paper you would recommend to better understand the project's direction, I would really appreciate it. Thank you for your time, looking forward to hearing from you. Best regards, Nikitas Rafail Karachalios
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