# Semantic Detection Training Data

# Source
- Aerial imagery from reconnaissance winged UAVs at 600–1000m altitude
- ViewPro A40 camera, 1080p resolution, various zoom levels
- Extracted from video frames and still images

# Target Classes
- Footpaths / trails (linear features on snow, mud, forest floor)
- Fresh footpaths (distinct edges, undisturbed surroundings, recent track marks)
- Stale footpaths (partially covered by snow/vegetation, faded edges)
- Concealed structures: branch pile hideouts, dugout entrances, squared/circular openings
- Tree rows (potential concealment lines)
- Open clearings connected to paths (FPV launch points)

# YOLO Primitive Classes (new)
- Black entrances to hideouts (various sizes)
- Piles of tree branches
- Footpaths
- Roads
- Trees, tree blocks

# Annotation Format
- Managed by existing annotation tooling in separate repository
- Expected: bounding boxes and/or segmentation masks depending on model architecture
- Footpaths may require polyline or segmentation annotation rather than bounding boxes

# Seasonal Coverage Required
- Winter: snow-covered terrain (footpaths as dark lines on white)
- Spring: mud season (footpaths as compressed/disturbed soil)
- Summer: full vegetation (paths through grass/undergrowth)
- Autumn: mixed leaf cover, partial snow

# Volume
- Target: hundreds to thousands of annotated images
- Available effort: 1.5 months, 5 hours/day
- Potential for annotation process automation

# Reference Examples
- semantic01.png — footpath leading to branch-pile hideout in winter forest
- semantic02.png — footpath to FPV launch clearing, branch mass at forest edge
- semantic03.png — footpath to squared hideout structure
- semantic04.png — footpath terminating at tree-branch concealment