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docs/03_tests/32_accuracy_50m_varied_terrain_spec.md

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+# Acceptance Test: 80% Photos <50m Error - Varied Terrain
+
+## Summary
+Validate AC-1 accuracy requirement (80% of photos within 50m error) across different terrain types including agricultural fields, mixed vegetation, and urban edges.
+
+## Linked Acceptance Criteria
+**AC-1**: The system should find out the GPS of centers of 80% of the photos from the flight within an error of no more than 50 meters in comparison to the real GPS.
+
+## Preconditions
+- ASTRAL-Next system fully deployed and operational
+- Satellite reference data downloaded for test region
+- TensorRT models loaded (SuperPoint, LightGlue, AnyLoc, LiteSAM)
+- Ground truth GPS coordinates available for validation
+- Test datasets covering varied terrain types
+
+## Test Data
+- **Primary Dataset**: AD000001-AD000060 (varied terrain across 60 images)
+- **Terrain Types**: Agricultural fields, tree lines, mixed vegetation, roads
+- **Ground Truth**: coordinates.csv
+- **Camera Parameters**: 400m altitude, 25mm focal length, 26MP resolution
+
+## Test Steps
+
+### Step 1: Initialize System with Starting GPS
+**Action**: Start flight processing with first image GPS coordinate (48.275292, 37.385220)
+**Expected Result**: 
+- System initializes successfully
+- Satellite tiles downloaded for operational area
+- L1, L2, L3 layers ready
+- Status: INITIALIZED
+
+### Step 2: Process Agricultural Field Segment (AD000001-015)
+**Action**: Process images over predominantly agricultural terrain
+**Expected Result**:
+- L1 sequential tracking maintains continuity
+- SuperPoint detects field boundaries and crop variations
+- LiteSAM achieves cross-view matching despite seasonal differences
+- Mean error <40m for this segment
+- Status: PROCESSING
+
+### Step 3: Process Mixed Vegetation Segment (AD000016-030)
+**Action**: Process images with mixed terrain features
+**Expected Result**:
+- L2 global place recognition active during transitions
+- AnyLoc retrieval successful using DINOv2 features
+- Factor graph optimization smooths trajectory
+- Mean error <45m for this segment
+- Status: PROCESSING
+
+### Step 4: Process Complex Terrain with Sharp Turns (AD000031-060)
+**Action**: Process remaining images including sharp turns and outliers
+**Expected Result**:
+- L2 recovers from sharp turns (AD000042-043, AD000032-033)
+- Robust cost functions handle AD000047-048 outlier (268.6m)
+- Multiple map fragments merged successfully
+- Mean error <50m for challenging segments
+- Status: PROCESSING
+
+### Step 5: Calculate Accuracy Metrics
+**Action**: Compare estimated GPS coordinates with ground truth
+**Expected Result**:
+```
+Total images: 60
+Error <50m: ≥48 images (80%)
+Error <20m: ≥36 images (60%)
+Mean error: <40m
+Median error: <35m
+Max error: <150m (excluding known outliers)
+```
+
+### Step 6: Validate Terrain-Specific Performance
+**Action**: Analyze accuracy by terrain type
+**Expected Result**:
+- Agricultural fields: 75-85% <50m
+- Mixed vegetation: 80-90% <50m  
+- Road intersections: 85-95% <50m
+- Overall: ≥80% <50m
+- Status: COMPLETED
+
+## Pass/Fail Criteria
+
+**PASS if**:
+- ≥80% of images (48/60) have error <50m
+- No systematic bias across terrain types
+- System completes without fatal errors
+- Factor graph converges (final MRE <1.5px)
+
+**FAIL if**:
+- <80% of images meet 50m threshold
+- >3 terrain types show <70% accuracy
+- System crashes or hangs
+- Catastrophic tracking loss without recovery
+
+## Performance Requirements
+- Processing time: <5 seconds per image average
+- Total flight time: <5 minutes for 60 images
+- Memory usage: <8GB on RTX 3070
+- CPU usage: <80% average
+
+## Notes
+- Varied terrain test provides more comprehensive validation than baseline
+- Different terrain types stress different system components
+- AC-1 threshold of 80% allows for difficult scenarios while maintaining operational utility
+