3 Commits

Author	SHA1	Message	Date
pikaliov	f0c876dfc7	Add segmentation post-processing: dark water fix + wetland reclassify ... Two heuristic rules applied after SegEarth-OV3 inference: 1. Dark water: if background pixels have mean_rgb < 0.24 and std < 0.08, reclassify as water. Fixes GTA-UAV satellite dark ocean (57% → ~15% bg). 2. Wetland reclassify (GTA-UAV only): split false-positive wetland pixels by color — green-dominant → vegetation, else → bare soil. Fixes 14.3% muddy/wetland false positives on GTA-V hillside terrain. Config flags: seg_fix_dark_water (default True), seg_reclassify_wetland (default False, enabled in run_gta_uav.py). Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>	2026-04-18 02:37:43 +03:00
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