artvandelay / street-addresses

Robocodes: Towards Generative Street Addresses from Satellite Imagery

Robocodes: Towards Generative Street Addresses from Satellite Imagery

This repo contains the code for creating generative street addresses from OSM input, as presented in our paper at the CVPR - EarthVision 2017. The naming procedure inputs .osm files, or geotiffs; and outputs new maps with hierarchical and linear addressing scheme.

Requirements

1. Install OpenCV => 2.4.8 C++ bindings for road_segmentor module.

Useful links for OpenCV installation:

• Ubuntu: https://github.com/milq/milq/blob/master/scripts/bash/install-opencv.sh
• OS X: http://www.pyimagesearch.com/2015/06/15/install-opencv-3-0-and-python-2-7-on-osx/

2. Install python dependencies (Python 2.7).

pip install -r requirements.txt

Building Robocodes

1. Clone the repo.

$ git clone https://github.com/facebookresearch/street-addresses.git

2. Change directory to ${ROBOCODE}/road_segmentor and build. (Cmake => 2.8)

$ cmake .
$ make

Generated binary will be stored in ${ROBOCODE}/road_segmentor/bin

3. Run the following command to change the library paths.

$ install_name_tool -add_rpath /<open_cv_lib_path>/ bin/RoadConnectionLabelling

4. Check the permissions of the main python scripts run_end2end.py and gen_robocode.py.

Examples

You can check additional functionalities with $ ./run_end2end.py --help. Below are some examples for easy robocode generation.

OSM Example: Running the script when the input is an OSM file. Creates an output osm file and additional query structure in the specified directory.

$ ./run_end2end.py \
 --xml ${ROBOCODE}/example/nashik.osm \
--out_dir /<output_dir>/ \
--roadSeg_bin ${ROBOCODE}/road_segmentor/bin/RoadConnectionLabelling 

Additional OSM files can be exported from OpenStreetMap: https://www.openstreetmap.org

TIFF Example: Running the script when the input is a GeoTiff file containing binary road masks. Creates an output osm file and additional query structure in the specified directory.

$ ./run_end2end.py \
--input_tiff ${ROBOCODE}/example/nashik.tif \
--out_dir /<output_dir>/ \
--roadSeg_bin ${ROBOCODE}/road_segmentor/bin/RoadConnectionLabelling

Geocoding Example: Generating Robocode when lat/lon is input.

$ ./gen_robocode.py \
-path /<input_dir>/ \
-lat 20.0226957656 \
-lon 73.7834041609 \
-city NASHIK

Adress: 388A.NA104.NASHIK

PS: your lat and lon input should be within range of (minlat, maxlat) and (minlon,maxlon) respectively as per your input OSM or Geotiff input.

Reverse Geocoding Example: Generating lat/lon when Robocode is input.

$ ./gen_robocode.py \
-path /<input_dir>/ \
-meter 374 \
-block B \
-street NA104 \
-city NASHIK

Lat, Lon: 20.0230511115, 73.7822889019

References

Please cite our CVPR 2017 - EarthVision Workshop paper below when using the code. https://research.fb.com/publications/robocodes-towards-generative-street-addresses-from-satellite-imagery/

@inproceedings{RobocodesCVPREV2017,
    title     = {Robocodes: Towards Generative Street Addresses from Satellite Imagery},
    author    = {Ilke Demir, Forest Hughes, Aman Raj, Kleovoulos Tsourides, Divyaa Ravichandran, Suryanarayana Murthy,
                 Kaunil Dhruv, Sanyam Garg, Jatin Malhotra, Barrett Doo, Grace Kermani, Ramesh Raskar},
    booktitle = {IEEE International Conference on Computer Vision and Pattern Recognition, EARTHVISION Workshop},
    year      = {2017} \
}

License

Robocodes project is licenced under CC-by-NC, see the LICENSE file for details.