- config_loader: add base_gin_files() to exclude dataset-specific
pipeline_*.gin variants from default `python -m src.main` load
(pipeline_uav_visloc.gin was overriding pipeline.gin)
- main.py: use base_gin_files() in the --gin override branch too
- pipeline.gin: source None (both DB + query) — was 'query',
which silently dropped all DB satellite crops from discovery
- README/CLAUDE/docs: sync source default, tensor format spec
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Verified on 128 satellite images after dark water fix (std threshold
0.08 → 0.18). Document calibrated thresholds and measured improvements.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Extract shared UNIFIED_PROMPTS (17 classes, ID 0-16) into
scripts/seg_classes.py for transfer learning compatibility.
Both run_gta_uav.py and run_uav_visloc.py now import from it.
Key change: swimming pool moved from ID 13 → ID 16, so sports field
(ID 13), muddy ground (14), embankment (15) have stable IDs across
both datasets. Missing classes in a dataset = 0 pixels = 0 loss.
Updated: README, CLAUDE.md, segmentation_class_analysis.md, palette.
Deleted old UAV_VisLoc segmentations (need regeneration with 17 classes).
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
- Add scripts/run_gta_uav.py for GTA-UAV-LR (48K images, GTA V synthetic)
- 14 segmentation classes: 11 base + bare soil, rooftop, swimming pool
- Fix source filter to recognize "satellite" folder (alongside "DB")
- Document GTA-UAV characteristics in segmentation_class_analysis.md
- Update README and CLAUDE.md with GTA-UAV support
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
