Strengthening Remote Sensing Manuscript Methodology for AI Journal submission

I am preparing a manuscript for submission to a peer-reviewed journal in remote sensing / geospatial AI. The work integrates large language models (LLMs), retrieval-augmented generation (RAG), and computer vision for overhead imagery analysis. The system is implemented and preliminary performance results exist, but the manuscript has received editorial rejections due to insufficient methodological framing and experimental rigor. I am seeking a PhD-level expert in remote sensing, computer vision, or geospatial machine learning to strengthen the experimental design and validation. Scope of work: • Review current manuscript and experimental claims • Define clear research questions and hypotheses • Design appropriate baseline comparisons (e.g., CV-only vs. CV+RAG vs. full system) • Structure controlled experiments and dataset splits • Develop ablation study design • Apply appropriate statistical testing and validation methods • Improve reproducibility and clarity of the methodology section • Advise on positioning for IEEE / ISPRS-level journals Deliverables: • Revised experimental framework • Clear validation plan • Statistical analysis guidance • Marked-up methodology section with specific recommendations This is not a writing or editing task. It is a research methodology and validation engagement focused on meeting high-impact journal standards.