# Question Decomposition

## Original Question
Should LiteSAM replace the current SuperPoint+LightGlue satellite fine matching stage in the GPS-denied UAV navigation pipeline, and how does it compare to EfficientLoFTR?

## Active Mode
Mode B — Solution Assessment of solution_draft03.md, focused on satellite geo-referencing component.

## Problem Context Summary
- UAV photos (FullHD to 6252×4168) from wing-type UAV, camera pointing down
- Eastern/southern Ukraine, no IMU data, altitude ≤1km
- GPS of first image known, need to determine GPS for all subsequent images
- Two-stage satellite matching: DINOv2 ViT-S/14 coarse retrieval + SuperPoint+LightGlue ONNX FP16 fine matching
- Hardware constraint: RTX 2060 (6GB VRAM), 16GB RAM
- Processing target: <5s per image (current estimate ~230-270ms)

## Question Type
Decision Support — weighing trade-offs between matching approaches for the satellite geo-referencing component.

## Research Subject Boundary
| Dimension | Boundary |
|-----------|----------|
| Population | Satellite-to-aerial (nadir UAV) image matching at 100-1000m altitude |
| Geography | Rural/agricultural terrain, Eastern Ukraine |
| Timeframe | 2024-2026, focusing on current state-of-the-art |
| Level | Production deployment on RTX 2060 GPU |

## Decomposed Sub-questions
1. How does LiteSAM's accuracy compare to SuperPoint+LightGlue and EfficientLoFTR on satellite-aerial benchmarks?
2. What is the estimated inference time on RTX 2060 for each approach?
3. What is the VRAM footprint of each approach?
4. How mature is each codebase for production deployment (ONNX, TensorRT, community)?
5. How do these approaches handle rotation variance (critical for segment starts)?
6. Can LiteSAM/EfficientLoFTR coexist with the DINOv2 coarse retrieval stage?
7. What are the risks of adopting LiteSAM given its early-stage codebase?

## Timeliness Sensitivity Assessment

- **Research Topic**: Feature matching for satellite-UAV localization
- **Sensitivity Level**: 🟠 High
- **Rationale**: Active research area with new methods published monthly; model capabilities and benchmarks change rapidly
- **Source Time Window**: 12 months
- **Priority official sources**:
  1. LiteSAM paper (Oct 2025) + GitHub repo
  2. EfficientLoFTR paper (CVPR 2024) + GitHub repo
  3. LightGlue-ONNX GitHub repo
- **Key version information**:
  - LiteSAM: v1 (initial release, 4 commits)
  - EfficientLoFTR: CVPR 2024, stable
  - LightGlue-ONNX: active development
