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Boundary-Aware Feature Computation

Cross-tile processing that eliminates edge artifacts and ensures consistent feature quality across tile boundaries.

🎯 What is Boundary-Aware Processing?​

Boundary-aware processing uses neighboring tile context when computing features near tile edges, eliminating the artificial discontinuities that occur when tiles are processed in isolation.

The Problem​

Traditional tile-by-tile processing creates artifacts at boundaries:

Tile A          |  Tile B
                |
Points near edge have:
- Incomplete neighborhoods
- Incorrect geometric features
- Edge artifacts in ML predictions

The Solution​

Boundary-aware processing loads neighbor tiles and includes their points when computing features:

Tile A + Buffer from B  |  Tile B + Buffer from A
                        |
Points near edge now have:
✅ Complete neighborhoods
✅ Accurate geometric features
✅ Seamless predictions across tiles

🚀 Usage​

Enable Boundary-Aware Processing​

# Basic usage
ign-lidar-hd process \
  input_dir=data/raw/ \
  output_dir=output/ \
  features.boundary_aware=true

With Tile Stitching​

Boundary-aware processing works seamlessly with tile stitching:

# Multi-tile dataset with boundary-aware features
ign-lidar-hd process \
  input_dir=data/raw/ \
  output_dir=output/ \
  stitching.enabled=true \
  features.boundary_aware=true \
  features.buffer_size=5.0

⚙️ Configuration​

Key Parameters​

ParameterTypeDefaultDescription
features.boundary_awareboolfalseEnable boundary-aware processing
features.buffer_sizefloat5.0Buffer distance in meters
features.neighbor_tilesint8Number of neighbors to consider (1-8)
features.edge_thresholdfloat2.0Distance from edge to apply buffer

Buffer Size Selection​

Choose buffer size based on your feature scale:

# For local features (curvature, normal vectors)
features.buffer_size=2.0

# For neighborhood features (linearity, planarity)
features.buffer_size=5.0

# For contextual features (density, height variations)
features.buffer_size=10.0

📊 How It Works​

1. Neighbor Detection​

2. Buffer Extraction​

# For each neighbor tile
1. Load neighbor point cloud
2. Extract points within buffer_size of shared boundary
3. Merge with current tile points
4. Compute features with complete neighborhood

3. Feature Computation​

# Compute features with neighbor context
for point in tile_points:
    if distance_to_edge(point) < edge_threshold:
        neighborhood = get_neighbors(point, include_buffer=True)
    else:
        neighborhood = get_neighbors(point, include_buffer=False)

    features = compute_features(neighborhood)

🎨 Visual Comparison​

Without Boundary-Aware Processing​

Tile Boundary Issue:
┌─────────────┬─────────────┐
│   Tile A    │   Tile B    │
│             │             │
│        XXXXX│XXXXX        │  ← Artifacts at edge
│        XXXXX│XXXXX        │
│             │             │
└─────────────┴─────────────┘

With Boundary-Aware Processing​

Seamless Transition:
┌─────────────┬─────────────┐
│   Tile A    │   Tile B    │
│             │             │
│   XXXXXXXXXXXXXXXXXX      │  ← Smooth features
│   XXXXXXXXXXXXXXXXXX      │
│             │             │
└─────────────┴─────────────┘

📈 Performance Impact​

Processing Time​

ModeTime per TileOverhead
Standard1.0x0%
Boundary-Aware (1-4)1.1x+10%
Boundary-Aware (5-8)1.2x+20%

Memory Usage​

Memory per tile = tile_size + (buffer_size × num_neighbors)

Example (50m tile, 5m buffer, 4 neighbors):
  Standard: 100 MB
  Boundary-aware: 120 MB (+20%)

🔧 Advanced Usage​

Custom Neighbor Selection​

# Only load orthogonal neighbors (faster)
ign-lidar-hd process \
  input_dir=data/ \
  output_dir=output/ \
  features.boundary_aware=true \
  features.neighbor_tiles=4

# Load all 8 neighbors (highest quality)
ign-lidar-hd process \
  input_dir=data/ \
  output_dir=output/ \
  features.boundary_aware=true \
  features.neighbor_tiles=8

Python API​

from ign_lidar.features import BoundaryAwareComputer
from ign_lidar.io import TileManager

# Initialize
tile_manager = TileManager(input_dir="data/raw/")
computer = BoundaryAwareComputer(
    buffer_size=5.0,
    neighbor_tiles=8,
    edge_threshold=2.0
)

# Process with boundary awareness
tile = tile_manager.load_tile("tile_1234_5678")
neighbors = tile_manager.get_neighbors(tile)
features = computer.compute_features(tile, neighbors)

print(f"Features computed with {len(neighbors)} neighbors")

✅ Best Practices​

When to Use​

✅ Use boundary-aware processing when:

  • Processing multi-tile datasets
  • Features depend on local neighborhoods
  • Quality at boundaries is critical
  • Training ML models on tile data

❌ Skip boundary-aware processing when:

  • Processing single tiles
  • Using global features only
  • Performance is critical
  • Tiles have no neighbors

Optimization Tips​

  1. Start small: Use neighbor_tiles=4 for faster processing
  2. Tune buffer: Match buffer size to feature scale
  3. Batch processing: Process tiles in groups for efficiency
  4. Cache neighbors: Reuse neighbor data when processing adjacent tiles

🐛 Troubleshooting​

Missing Neighbors​

# Error: Cannot find neighbor tiles
Solution: Ensure all neighbor tiles are available in input_dir

# Check tile availability
ls -la data/raw/*.laz | wc -l

High Memory Usage​

# Reduce buffer size
features.buffer_size=2.0

# Or reduce neighbors
features.neighbor_tiles=4

Slow Processing​

# Process fewer neighbors
features.neighbor_tiles=4

# Or reduce buffer
features.buffer_size=3.0

# Or disable for non-edge points
features.edge_threshold=5.0  # Only process within 5m of edge

📚 Related Topics​

🎓 Example Workflows​

Regional Dataset with Boundary-Aware Features​

# 1. Download regional tiles
ign-lidar-hd download \
  bbox="2.3,48.8,2.4,48.9" \
  output_dir=data/raw/

# 2. Process with boundary-aware features
ign-lidar-hd process \
  input_dir=data/raw/ \
  output_dir=output/ \
  stitching.enabled=true \
  features.boundary_aware=true \
  features.buffer_size=5.0 \
  features=full

# 3. Results: Seamless features across all tiles

Next Steps: