MapFuse

MapFuse solves the problem of fusing two different maps in order to highlight commonalities and differences between them. A relevant use case of MapFuse would be fuse an existing outdated yet high quality map representing a road network with a more recent map created from GPS traces in order to update the existing one.

The solution takes as input a shapefile of a map (say osm) and a csv file of GPS points. It generates a new shapefile or geojson file of the fused map which highlights new segments and dead segments.

To learn more about the project, check our paper: Rade Stanojevic, Sofiane Abbar, Saravanan Thirumuruganathan, Gianmarco De Francisci Morales, Sanjay Chawla, Fethi Filali and Ahid Aleimat: Road Network Fusion for Incremental Map Updates. In special volume of Springer's Lecture Notes in Cartography and Geoinformation (LBS 2018).

Running the code:

Requirements

The easiest way to run the code is to use virtualenv.

• install virtualenv: sudo apt-get install virtualenv
• Head to a directory where you want to create a virtualenv and run: virtualenv --no-site-packages qmap_venv
• Activate the newly created virtualenv as follows: source qmap_venv/bin/activate
• Install all packages in requirements.txt as follows: pip install -r requirements.txt Once virtualenv is activated, proceed with the following to see the different options: python MapFuse.py -h

Then run:

python MapFuse.py -d data/gps_data.csv -b data/osm/doha_qatar_osm_roads.shp -t 2015-11-01 -p 1

• -d: path to the csv data file in the format: speed, data_time, bearing, lon, lat
• -b: path to the shape file of the base map (OSM, etc.)
• -t: the starting date (yyyy-mm-dd) of the gps points to consider in the fusion
• -p: 1 to plot the fused map, 0 not to plot it.

The output fused map is saved into data/fused_map.geojson

IMPORTANT:

The input gps_data file needs to be sorted by trajectory_id (or vehicle_id), then date_time. This is very important to make sure we create correct trajectories internally.

For instance, assume you have a file (file_1.csv) with the following format: traj_id, angle, data_time, speed, lon, lat.

1- You first need to sort it this way: sort -t',' -k1,1 -k3,3 file_1.csv > file_2.csv

2- Remove the traj_id information: cut -d',' -f2,3,4,5,6 file_2.csv > file_3.csv

3- Use this file_3.csv as the input for MapFuse.

NB:

• bearing: is the angle from north, values in [0: 360]. If not available, then you can infer it from successive points.

• speed: in km/h, can be inferred from the data as well.

Visualizing the output:

The fusion of the two maps can be rendered as follows:

• Green: Base map (OSM)
• Red: newly detected road segments