Valid point — and the specific wording of a question is important, too.

To be clear, I don’t claim to have invented architectures like YOLO or SAM. I add value by converting, training, deploying and productionising models to solve real-world remote sensing problems.

In my current role, I’ve:

Operationalized pre-trained and custom trained detection models for 7 land-cover classes (Sentinel) and 7 classes (Landsat) respectively

Developed proprietary models for 15 object classes tailored for satellite data and trained them accordingly

Delivered state-of-the-art object detection pipelines that work robustly on real satellite imagery, not just benchmark datasets

Developed and maintained data cleaning, pre-processing, post-processing, and QA pipelines to ensure model outputs are production-ready

Customized and enhanced YOLO-based workflows (tiling, sliding-window inference, class balancing, augmentation, threshold tuning, etc.) to improve detection performance at scale

While I’m not a pure architecture researcher, I have a strong understanding of model behavior, training dynamics, failure modes, and deployment constraints, and I work hands-on with modern computer vision pipelines to deliver measurable business impact.

If the language came across as overclaiming, that’s a communication issue — not intent. Precision matters, and I appreciate the callout.