Dear Mentors, Thank you for the clarifying update regarding the "Open-Source AI Framework for Thermal Satellite Payload Data Analysis" project. It is incredibly helpful for contributors' proposals. My name is Thanos. I hold a PhD in Physics (specializing in Quantum Optics), am currently pursuing my Master's in Machine Learning and AI at the Xarokopeio University of Athens, and have more than 3 years of professional software engineering experience. Given my background in physics it would give me a solid understanding of optical sensors, signal-to-noise ratios, and the physical reality of thermal infrared data, while my current PyTorch/Computer Vision coursework and software engineering experience allow me to build production-ready pipelines and implement advanced explainability techniques. I am currently reviewing the /TIRAuxCloud repository to align my proposal with your existing cloud detection modelling code. To ensure my proposal is as realistic and technically clear as you requested, I have two brief questions: 1. Preprocessing Focus: For the dataset expansion and preprocessing pipeline, are you primarily looking for programmatic data augmentation and cleaning, or are you also looking to incorporate physical/radiometric calibration steps for the thermal data? 2. Explainability Integration: For methods like Grad-CAM++, is the goal to output visual heatmaps for researchers to interpret, or to use the explainability metrics to actively filter/weight the training data in a semi-supervised loop? Thank you again for the guidance. I look forward to submitting a solid and realistic proposal. Best regards, Thanos Smponias
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