Autoplotter With: Road Estimator Crack

An autoplotter, also known as an automatic plotter or pen plotter, is a device used to produce high-quality, large-format plots. Initially, these were mechanical devices that used pens to draw directly on paper. However, with advancements in technology, modern autoplotters are often software-based, integrating seamlessly with CAD and GIS applications. They enable users to automatically generate complex plots from digital designs or maps, saving time and reducing the effort required for manual plotting.

import rasterio as rio
import torch
from autoplotter import RoadVectorizer, Preprocessor, SegModel
# 1️⃣ Load a COG tile (256 Mpx max per job)
with rio.open("s3://my-bucket/ortho/2025-06/region_01.tif") as src:
    img = src.read(window=rio.windows.Window(col_off=0, row_off=0, width=1024, height=1024))
    transform = src.window_transform(rio.windows.Window(0,0,1024,1024))
# 2️⃣ Pre‑process (normalize + DEM flatten)
proc = Preprocessor()
img_norm = proc.normalize(img)
# 3️⃣ Predict road mask
model = SegModel("weights/deeplabv3_asphalt.pth")
with torch.no_grad():
    mask = model.predict(img_norm)   # shape (H, W), binary road mask
# 4️⃣ Vectorize
vectorizer = RoadVectorizer(mask, transform)
gdf = vectorizer.extract_vectors(min_length=2.0, simplify_tol=0.5)
# 5️⃣ Save
gdf.to_file("output/road_vectors.gpkg", driver="GPKG")

Tip: Deploy the above as a AWS Lambda or Google Cloud Function triggered by new COG uploads. The function returns a signed URL to the generated vector file, enabling downstream pipelines to start immediately. autoplotter with road estimator crack

A road estimator is a specialized software tool designed to work within the construction and civil engineering sectors. It helps in estimating quantities and costs associated with road construction projects. This includes calculating the volume of materials needed, labor costs, and providing a detailed breakdown of expenses. When integrated with an autoplotter, the road estimator can significantly enhance the plotting process by providing accurate measurements and estimates directly within the plot. An autoplotter, also known as an automatic plotter

A practical guide to building, deploying, and scaling a fully automated pavement‑crack‑mapping pipeline. Tip: Deploy the above as a AWS Lambda

| Challenge | Autoplotter alone | Road‑Estimator alone | Combined solution | |-----------|-------------------|----------------------|-------------------| | Noisy raster → vector conversion | Handles geometry, but cannot infer surface condition. | Needs clean road geometry to bound analysis. | Autoplotter supplies clean lines; Estimator focuses on condition. | | Scalability | Can process city‑wide mosaics in minutes using GPU‑accelerated raster pipelines. | Typically run on per‑segment tiles; scaling bottleneck without pre‑segmentation. | Autoplotter partitions the raster into road‑aligned tiles automatically → embarrassingly parallel Estimator jobs. | | Attribute linkage | Provides lane, width, surface type attributes, but no wear data. | Produces crack polygons that are “floating” in image space. | Directly joins crack geometry to the nearest road segment, inheriting all road attributes. | | Regulatory reporting | Generates GIS‑ready layers but no condition grades. | Outputs probability maps that need manual interpretation. | Generates ready‑to‑publish GIS layers with crack severity and maintenance priority fields. |

Benefits:

Drawbacks: