Autoplotter With Road Estimator Crack (Official)

| Component | Core Function | Typical Input | Typical Output | |-----------|---------------|---------------|----------------| | Autoplotter | High‑throughput raster → vector conversion, geometric cleaning, and map‑ready rendering. | Orthophotos, LiDAR‐derived DEMs, satellite imagery (GeoTIFF, Cloud‑Optimized GeoTIFF). | GeoJSON / Shapefile road network, lane centrelines, shoulder polygons, attribute tables. | | Road‑Estimator | Machine‑learning based road‑surface condition estimator (roughness, texture, and especially crack detection). | Aligned road‑centerline vectors + high‑resolution surface imagery (e.g., 0.05 m/pixel UAV orthophotos). | Per‑segment crack probability, crack geometry (polylines), severity scores, confidence intervals. | | Integration Layer | Orchestrates data flow, spatial joins, and quality‑control (QC) reporting. | Outputs from the two modules above. | Final “crack‑map” product ready for GIS, asset‑management, or autonomous‑vehicle (AV) simulation. |

Bottom line: By feeding the clean, topology‑aware road vectors from Autoplotter into a Road‑Estimator model, you get pixel‑accurate crack geometries that are automatically linked to the underlying road network. The result is a single, up‑to‑date geospatial dataset that can feed maintenance planning, budgeting, and AI‑driven driving‑simulation pipelines. autoplotter with road estimator crack

+-------------------+   1. Acquire imagery (UAV/airborne)   +--------------------+
|   Raw COG Tiles   |------------------------------------->|   Autoplotter      |
+-------------------+                                    +--------------------+
          |                                                    |
          | 2. Clean road vectors (GeoPackage)                |
          v                                                    v
+-------------------+   3. Buffer & clip per road segment   +--------------------+
|   Road Vectors    |<------------------------------------|   Clip & Align     |
+-------------------+                                    +--------------------+
          |                                                    |
          | 4. Run Road‑Estimator on each chip                |
          v                                                    v
+-------------------+   5. Crack polygons & severity       +--------------------+
|   Crack GeoJSON   |------------------------------------->|   QC Dashboard     |
+-------------------+                                    +--------------------+
          |
          | 6. Merge (spatial join) → Final product
          v
+-------------------+   7. Publish to GIS/Asset DB (PostGIS, ArcGIS)   +--------------------+
|  Final Crack Map  |---------------------------------------------------->|   Decision‑Support |
+-------------------+                                                    +--------------------+

from cracknet import DeepCrack
model = DeepCrack("weights/deepcrack_resnet.pth")
model.eval()
def infer_crack(chip):
    prob = model.predict(chip)          # (H, W) probability map
    binary = prob > 0.5                 # threshold
    # Morphological clean‑up
    cleaned = binary_opening(binary, disk(2))
    # Vectorize cracks (thin → skeleton → polygonize)
    cracks = rasterio.features.shapes(cleaned.astype('uint8'), transform=transform)
    # Convert to GeoDataFrame
    gdf = gpd.GeoDataFrame([
        "road_id": rid, "geometry": shape, "prob": prob.mean()
        for shape, value in cracks if value == 1
    ], crs="EPSG:3857")
    return gdf

Key post‑processing steps

| Step | Reason | |------|--------| | Morphological opening/closing | Removes spurious speckles, bridges tiny gaps (< 0.1 m). | | Skeletonization + line‑simplification | Produces clean polylines suitable for GIS. | | Confidence‑weighted filtering | Keeps only segments where prob > 0.7 or where the model’s uncertainty (Monte‑Carlo dropout) is low. | | Spatial join to road vector | Ensures each crack inherits road_id, lane_count, surface_type. | | Component | Core Function | Typical Input