# Question Decomposition — Solution Assessment (Mode B)

## Original Question
Assess the existing solution draft (solution_draft01.md) for weak points, security vulnerabilities, and performance bottlenecks, then produce a revised solution draft.

## Active Mode
Mode B: Solution Assessment — `solution_draft01.md` exists and is the highest-numbered draft.

## Question Type Classification
- **Primary**: Problem Diagnosis — identify weak points, vulnerabilities, bottlenecks in existing solution
- **Secondary**: Decision Support — evaluate alternatives for identified issues

## Research Subject Boundary Definition

| Dimension | Boundary |
|-----------|----------|
| **Domain** | GPS-denied UAV visual navigation, aerial geo-referencing |
| **Geography** | Eastern/southern Ukraine (left of Dnipro River) — steppe terrain, potential conflict-related satellite imagery degradation |
| **Hardware** | Desktop/laptop with NVIDIA RTX 2060+, 16GB RAM, 6GB VRAM |
| **Software** | Python ecosystem, GPU-accelerated CV/ML |
| **Timeframe** | Current state-of-the-art (2024-2026), production-ready tools |
| **Scale** | 500-3000 images per flight, up to 6252×4168 resolution |

## Problem Context Summary
- UAV aerial photos taken consecutively ~100m apart, camera pointing down (not autostabilized)
- Only starting GPS known — must determine GPS for all subsequent images
- Must handle: sharp turns, outlier photos (up to 350m gap), disconnected route segments
- Processing <5s/image, real-time SSE streaming, REST API service
- No IMU data available

## Decomposed Sub-Questions

### A: Cross-View Matching Viability
"Is SuperPoint+LightGlue with perspective warping reliable for UAV-to-satellite cross-view matching, or are there specialized cross-view methods that would perform better?"

### B: Homography-Based VO Robustness
"Is homography-based VO (flat terrain assumption) robust enough for non-stabilized camera with potential roll/pitch variations and non-flat objects?"

### C: Satellite Imagery Reliability
"What are the risks of relying solely on Google Maps satellite imagery for eastern Ukraine, and what fallback strategies exist?"

### D: Processing Time Feasibility
"Are the processing time estimates (<5s per image) realistic on RTX 2060 with SuperPoint+LightGlue+satellite matching pipeline?"

### E: Optimizer Specification
"Is the sliding window optimizer well-specified, and are there more proven alternatives like factor graph optimization?"

### F: Camera Rotation Handling
"How should the system handle arbitrary image rotation from non-stabilized camera mount?"

### G: Security Assessment
"What are the security vulnerabilities in the REST API + SSE architecture with image processing pipeline?"

### H: Newer Tools & Libraries
"Are there newer (2025-2026) tools, models, or approaches that outperform the current selections (SuperPoint, LightGlue, etc.)?"

### I: Segment Management Robustness
"Is the segment management strategy robust enough for multiple disconnected segments, especially when satellite anchoring fails for a segment?"

### J: Memory & Resource Management
"Can the pipeline stay within 16GB RAM / 6GB VRAM while processing 3000 images at 6252×4168 resolution?"

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## Timeliness Sensitivity Assessment

- **Research Topic**: GPS-denied UAV visual navigation using learned feature matching and satellite geo-referencing
- **Sensitivity Level**: 🟠 High
- **Rationale**: Computer vision feature matching models (SuperPoint, LightGlue, etc.) are actively evolving with new versions and competitors. However, the core algorithms (homography, VO, optimization) are stable. The tool ecosystem changes frequently.
- **Source Time Window**: 12 months (2025-2026)
- **Priority official sources to consult**:
  1. LightGlue / SuperPoint GitHub repos (releases, issues)
  2. OpenCV documentation (current version)
  3. Google Maps Tiles API documentation
  4. Recent aerial geo-referencing papers (2024-2026)
- **Key version information to verify**:
  - LightGlue: current version and ONNX/TensorRT support status
  - SuperPoint: current version and alternatives
  - FastAPI: SSE support status
  - Google Maps Tiles API: pricing, coverage, rate limits
