Integration of Depth-Enhanced Data Formats for Broader AI Applications

[EmptyMonad]

I’ve been working on a system (https://github.com/EmptyMonad/VitalVista) that applies depth mapping to data formats similar to multi-layered images (like GIF or 8-bit bitmap files), creating more individuated outputs for different wavelengths of electromagnetic data. The output files are similar in structure but have added depth information that differentiates various spectral data layers (e.g., infrared, UV).

I’m curious if this type of depth-enhanced data format would be useful in your project. Specifically, I’m wondering what type of electromagnetic data you are receiving for training—is it flat (2D like a standard bitmap) or does it calculate depth (e.g., as layers or with embedded Z-axis values)? I believe synthesizing these different approaches could lead to broader AI projects, potentially useful for expanding pattern recognition across the electromagnetic spectrum.

My interest lies in extending this approach to help large language models (LLMs) or other AI systems comprehend data across a broader range of electromagnetic spectrums, from infrared to ultraviolet and gamma. Does this align with your project's direction, and would it be something worth collaborating on?

[EmptyMonad]

When inverted, possibilities seem to expand.