# roadtracer

**Repository Path**: mirrors_mapbox/roadtracer

## Basic Information

- **Project Name**: roadtracer
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2020-08-09
- **Last Updated**: 2025-09-20

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

RoadTracer Code
===============

This is the code for ["RoadTracer: Automatic Extraction of Road Networks from Aerial Images"](https://roadmaps.csail.mit.edu/roadtracer/).

There are several components, and each folder has a README with more usage details:

* dataset: code for dataset preparation
* roadtracer: RoadTracer
* roadcnn: our segmentation approach (baseline)
* deeproadmapper: DeepRoadMapper (baseline)

You will need gomapinfer (https://github.com/mitroadmaps/gomapinfer/) as a dependency.

The training/inference code is built on top of TensorFlow.

Usage
-----

First, follow instructions in dataset/ to download the dataset.

Then, follow instructions in the other folders to train a model and run inference.

Junction Metric
---------------

The junction metric matches junctions (any vertex with three or more incident edges) between a ground truth road network graph and an inferred one.

	go run junction_metric.go /data/graphs/chicago.graph chicago.out.graph chicago

Visualization
-------------

`viz.go` will generate an SVG from a road network graph. It will refer to the `/data/testsat/` images; to view the SVG, those images will need to be in the same folder as the generated SVG.

	go run viz.go chicago /data/graphs/chicago.graph
	go run viz.go chicago chicago.out.graph

Applying RoadTracer on a new region
-----------------------------------

You need to make a few modifications to run the code on a region outside of the 40-city RoadTracer dataset.

First, download the imagery. Update `dataset/lib/regions.go` and put a latitude/longitude bounding box around your region in the regionMap. You can comment out the existing regions. Then, follow instructions in dataset/ for running `1_sat.go`.

Then, update `roadtracer/tileloader.py` and set `TRAINING_REGIONS` and `REGIONS` to just a list with your region label from the regionMap. Also update `REGION`, `TILE_START`, and `TILE_END` in `infer.py` (e.g. if your imagery tiles were saved as `xyz_-1_-1_sat.png` through `xyz_1_1_sat.png`, set `TILE_START = -1, -1` and `TILE_END = 2, 2`.

Finally, manually specify a starting location for RoadTracer in infer.py. Set `USE_TL_LOCATIONS = False`, `MANUAL_RELATIVE = 0, 0`, and set the manual points to the pixel positions of two points on the road network in `xyz_0_0_sat.png`. These two points should be close to each other (around SEGMENT_LENGTH apart) and best to be in the middle of a road.

Now you should be able to get a road network graph by running infer.py.

To convert this to latitude/longitude, you can use `dataset/convertarg.go`:

	go run convertarg.go YOUR_REGION_LABEL frompix out.graph out.lonlat.graph