3 Commits

Author	SHA1	Message	Date
pikaliov	3b5778e303	Unify segmentation classes (17) across GTA-UAV and UAV_VisLoc ... 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>	2026-04-17 22:06:10 +03:00
pikaliov	143a837c03	Add GTA-UAV-LR annotation script + dataset documentation ... - 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>	2026-04-17 21:55:56 +03:00
pikaliov	27eb315903	Update docs: CLAUDE.md, README, segmentation class analysis ... - Create CLAUDE.md with project overview, key decisions, structure - Update README: add UAV_VisLoc dataset, 16-class palette, scripts - Extend segmentation_class_analysis.md with UAV_VisLoc section: quantitative analysis of 2496 images, 5 new classes with detailed justification (bare soil, rooftop, sports field, wetland, embankment), threshold rationale, and irreducible background explanation Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>	2026-04-17 21:27:25 +03:00