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>

scripts/run_gta_uav.py

@@ -0,0 +1,116 @@
+#!/usr/bin/env python3
+"""Run annotation pipeline for GTA-UAV-LR dataset.
+
+GTA-UAV-LR: synthetic dataset from GTA V engine.
+- drone/images/: 33763 images, 512x384, RGB PNG
+- satellite/:    14640 images, 256x256, RGBA PNG (alpha = map boundary)
+- Total: 48403 images
+- 6 flight heights: 100, 200, 300, 400, 500, 600 meters
+
+Usage:
+    python scripts/run_gta_uav.py
+    python scripts/run_gta_uav.py --source db       # only satellite (14.6K)
+    python scripts/run_gta_uav.py --source drone     # only drone (33.8K)
+"""
+
+from __future__ import annotations
+
+import argparse
+import sys
+from pathlib import Path
+
+_PROJECT_ROOT = Path(__file__).resolve().parent.parent
+sys.path.insert(0, str(_PROJECT_ROOT))
+
+import numpy as np
+import torch
+
+from src.conf.hardware_conf import HardwareConfig
+from src.conf.input_conf import InputConfig
+from src.conf.models_conf import ModelsConfig
+from src.conf.pipeline_conf import PipelineConfig
+from src.conf.seg_conf import SegConfig
+from src.augmentor.io_utils import setup_logging
+from src.main import run_pipeline
+
+
+INPUT_ROOT = "/home/servml/Документы/datasets/GTA-UAV-LR"
+OUTPUT_ROOT = "/home/servml/Документы/datasets/GTA-UAV-LR-aug"
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(description="Annotate GTA-UAV-LR")
+    parser.add_argument("--source", choices=["db", "drone", "all"], default="all",
+                        help="Process only db (satellite), drone, or all (default)")
+    parser.add_argument("--stages", nargs="+",
+                        default=["depth", "edges", "segmentation", "chmv2"],
+                        help="Stages to run")
+    args = parser.parse_args()
+
+    import gin
+    gin.clear_config()
+
+    source = None if args.source == "all" else args.source
+    if source == "drone":
+        source = "query"
+
+    pipeline_conf = PipelineConfig(
+        input_root=INPUT_ROOT,
+        output_root=OUTPUT_ROOT,
+        stages=args.stages,
+        save_npy=False,
+        save_vis=True,
+        save_safetensors=True,
+        cleanup_npy=True,
+        resume=True,
+        source=source,
+        log_level="INFO",
+    )
+
+    hw_conf = HardwareConfig(
+        profile_name="rtx4090",
+        total_ram_gb=24.0,
+        reserve_gb=2.0,
+        use_fp16=True,
+        batch_size=None,
+        num_workers=4,
+    )
+
+    # GTA-UAV: satellite 256x256, drone 512x384
+    # Use 256 for satellite, 512 for drone (non-square → resize to square)
+    input_conf = InputConfig(image_size=256, query_image_size=512)
+
+    # GTA V synthetic scenes: urban, suburban, rural, coastal, mountainous
+    # 11 base classes + pool (swimming pools common in GTA suburbs)
+    seg_conf = SegConfig(threshold=0.15, prompts=[
+        "background",                   # 0
+        "building",                     # 1
+        "road",                         # 2
+        "vegetation",                   # 3
+        "water",                        # 4
+        "sand and gravel ground",       # 5
+        "rocky terrain",                # 6
+        "farmland",                     # 7
+        "railway",                      # 8
+        "parking lot",                  # 9
+        "sidewalk",                     # 10
+        "bare soil and plowed field",   # 11
+        "roof and rooftop",             # 12
+        "swimming pool",               # 13
+    ])
+
+    models_conf = ModelsConfig(weights_dir=str(_PROJECT_ROOT / "in" / "weights"))
+
+    setup_logging(
+        pipeline_conf.log_level,
+        log_file=Path(OUTPUT_ROOT) / "pipeline.log",
+    )
+
+    torch.manual_seed(42)
+    np.random.seed(42)
+
+    run_pipeline(pipeline_conf, hw_conf, models_conf, input_conf, seg_conf)
+
+
+if __name__ == "__main__":
+    main()