Roadster: Distance to Closest Road Feature Server

Milliarium Aerum, the zero of all Roman roads

This is a feature server that computes the distance to the closest road as feature for a given coordinate.

It can be used command-line or with the provided Flask microframework server.

Roadster uses .SHP files from the Open Street Map project, as generously provided by Geofabrik GmbH.

It can provide the value of the distance-to-closest-road feature for a given coordinate or compute the value of the feature for all points on a given tile. If a given tile is too big, it can sample the feature and interpolate its value using Ordinary Kriging.

No pandas dependencies.

Installation

python setup.py install

Download the SHP files and put them (in .zip format) in the download/ folder.

Command-line

roadster-one-tile -m {map-prefix} -l {road_layer} -r {road_type} -z -w {tile-width} -h {tile-height} -o output_file -t {brute|kriging} -b {boost} west-north-gps-lat west-north-gps-long west-north-gps-lat east-south-gps-long

roadster-one-coord -m {map-prefix} -l {road_layer} -r {road_type} gps-lat gps-long

Arguments:

• Map prefix, e.g., malta-latest-free, the .shp.zip extension is added. This file is searched for in download folder. If the parameter contains a file path, the file is used verbatim (and needs to include the extension).
• Layer that contains the roads by default the first layer is used (which most probably do not contain the roads).
• Type of roads (e.g., 'primary'), by default use all roads.
• Set the roads distance to zero (otherwise is set to 1).
• Tile width and heights, in pixels.
• Output file, contains the extension, any of the formats understood by scikit-image.
• Type of computation of the tile, either brute force (distance computed for each pixel) or ordinary Kriging interpolation. If not specified, brute-force is used for tiles up to 128x128 pixels.
• Boost to the signal, defaults to 1000.0, try smaller numbers is the image is all white.

Finding the layer with roads

Use the provided command list-layers -m {map-prefix}.

API

import roadster

mapdata = roadster.data.load_map('prince-edward-island-latest-free', 11, set(['primary'])) # major roads
featdist = roadster.point.distance(mapdata, (-63.1293, 46.2905)) # long/lat
# ... use feature as needed ...
img = roadster.data.create_image(128, 128)
roadster.tile.plot(img, mapdata, 'brute', (-63.1647, 46.2779), (-63.0914, 46.2329), boost=10)
roadster.data.plot_roads(img, mapdata, (-63.1647, 46.2779), (-63.0914, 46.2329), road_value=1.0)
# ... use image as needed ...
roadster.data.save_image(img, "pei.png")

This computes a feature tile at this location: https://osm.org/go/cgtSeJX

The output tile looks like this:

Server

The Flask server takes similar parameters as the command-line interfaces. It caches the map data between calls.

flask run &
curl --output pei.png http://localhost:5000/tile/prince-edward-island-latest-free/11/128/128/46.2779/-63.1647/46.2329/-63.0914?zero_roads\&road_type=primary\&boost=10\&type=kriging
curl http://localhost:5000/point/prince-edward-island-latest-free/11/46.2779/-63.1647

Roadmap

• Handle downloading and caching OSM street data
• Better sampling for Kriging

Known issues

• The tool takes the coordinates in lon/lat format as that seems to be the format present in OSM
• On larger tiles, there are some projection issues that need to be debugged
• Truly large tiles (2048x2048) need 50Gb or more RAM to process
• Brute force does not use multiprocessing

Contributing

Pull requests are welcome! If you find Roadster useful, feel free to contribute. This codebase uses black for code formatting, please run it on your files before submitting them.

Acknowledgements

This project started at the #CleanMaltaAI hackathon as part of the Presagis team.

Citing

This code expands on Chapter 10: Other domains Video, geographic information and preferences of The Art of Feature Engineering. If you use this featurizer in your work, please cite:

@book{duboue2020art,
  title={The Art of Feature Engineering: Essentials for Machine Learning},
  author={Duboue, Pablo},
  year={2020},
  month={June},
  isbn={978-1108709385},
  publisher={Cambridge University Press},
  url={http://artoffeatureengineering.com}
}