add tests

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solution.md updated

docs/03_tests/31_accuracy_50m_baseline_spec.md

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+# Acceptance Test: AC-1 - 80% of Photos < 50m Error
+
+## Summary
+Validate Acceptance Criterion 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."
+
+## Linked Acceptance Criteria
+**AC-1**: 80% of photos < 50m error
+
+## Preconditions
+1. ASTRAL-Next system fully operational
+2. All TensorRT models loaded
+3. Satellite tiles cached for test area (48.25-48.28°N, 37.34-37.39°E)
+4. Ground truth GPS coordinates available (coordinates.csv)
+5. Test dataset prepared: Test_Baseline (AD000001-AD000030)
+
+## Test Description
+Process baseline flight of 30 images with normal spacing (~120m between images). Compare estimated GPS coordinates against ground truth and verify that at least 80% achieve error < 50 meters.
+
+## Test Steps
+
+### Step 1: Initialize System
+- **Action**: Start ASTRAL-Next system, verify all components ready
+- **Expected Result**: System state = "ready", all models loaded, no errors
+
+### Step 2: Create Test Flight
+- **Action**: Create flight "AC1_Baseline" with start_gps=48.275292, 37.385220, altitude=400m
+- **Expected Result**: Flight created, flight_id returned
+
+### Step 3: Upload Test Images
+- **Action**: Upload AD000001-AD000030 (30 images) in order
+- **Expected Result**: All 30 images queued, sequence maintained
+
+### Step 4: Monitor Processing
+- **Action**: Monitor flight status until completed
+- **Expected Result**: 
+  - Processing completes within 150 seconds (5s per image)
+  - No system errors
+  - Registration rate > 95%
+
+### Step 5: Retrieve Results
+- **Action**: GET /api/v1/flights/{id}/results
+- **Expected Result**: Results for all 30 images returned
+
+### Step 6: Calculate Errors
+- **Action**: For each image, calculate haversine distance between estimated and ground truth GPS
+- **Expected Result**: Error array with 30 values
+
+### Step 7: Validate AC-1
+- **Action**: Count images with error < 50m, calculate percentage
+- **Expected Result**: **≥ 80% of images have error < 50 meters** ✓
+
+### Step 8: Generate Report
+- **Action**: Create test report with statistics
+- **Expected Result**: 
+  - Total images: 30
+  - Images < 50m: ≥ 24
+  - Percentage: ≥ 80.0%
+  - Mean error: documented
+  - Median error: documented
+  - Max error: documented
+
+## Success Criteria
+
+**Primary Criterion (AC-1)**:
+- ≥ 24 out of 30 images (80%) have GPS error < 50 meters
+
+**Supporting Criteria**:
+- All 30 images processed (or user input requested if failures occur)
+- Processing time < 150 seconds total
+- No system crashes or unhandled errors
+- Registration rate > 95% (AC-9)
+
+## Expected Results
+
+Based on solution architecture (LiteSAM RMSE ~18m), expected performance:
+```
+Total Images: 30
+Successfully Processed: 30 (100%)
+Images with error < 50m: 28 (93.3%)
+Images with error < 20m: 20 (66.7%)
+Mean Error: 24.5m
+Median Error: 18.2m
+RMSE: 28.3m
+Max Error: 48.7m
+AC-1 Status: PASS (93.3% > 80%)
+```
+
+## Pass/Fail Criteria
+
+**TEST PASSES IF**:
+- ≥ 80% of images achieve error < 50m
+- System completes processing without critical failures
+- Results reproducible across multiple test runs
+
+**TEST FAILS IF**:
+- < 80% of images achieve error < 50m
+- System crashes or becomes unresponsive
+- More than 5% of images fail to process (violates AC-9)
+
+## Notes
+- This test uses Test_Baseline dataset (AD000001-030) with consistent spacing
+- No sharp turns or outliers in this dataset
+- Represents ideal operating conditions
+- If test fails, investigate: satellite data quality, model accuracy, georeferencing precision
+