python feature_extraction.py ../data/conf_tdrive_sample.json

python train.py --name t2rnet_tdrive_sample --dataroot ../data/tdrive_sample/learning --lam 0.2 --batch_size 8 --model t2rnet --display_id -1

python test.py --name t2rnet_tdrive_sample --dataroot ../data/tdrive_sample/learning --lam 0.2 --model t2rnet

python region_concatenation.py --tile_path ./results/t2rnet_tdrive_sample/test_latest/images/ --conf_path ../data/conf_tdrive_sample.json --results_path ../data/tdrive_sample/results/

python main.py --phase 1 --conf_path ../data/conf_tdrive_sample.json --results_path ../data/tdrive_sample/results/

python main.py --phase 2 --conf_path ../data/conf_tdrive_sample.json --results_path ../data/tdrive_sample/results/

python main.py --phase 3 --conf_path ../data/conf_tdrive_sample.json --results_path ../data/tdrive_sample/results/

python main.py --phase 4 --conf_path ../data/conf_tdrive_sample.json --results_path ../data/tdrive_sample/results/

DeepMG uses the following dependencies with Python 3.6

• gdal==2.3.2
• opencv==3.3.1
• rtree==0.8.3
• networkx==2.3
• scikit-image==0.16.2
• pytorch==1.1.0
• torchvision==0.3.0

Other packages can be easily installed using conda install, while the following scripts are recommended for gdal, opencv and pytorch.

conda install -c conda-forge gdal==2.3.2

conda install -c menpo opencv==3.3.1

conda install pytorch==1.1.0 torchvision==0.3.0 cudatoolkit=9.0 -c pytorch -c defaults -c numba/label/dev

Note that gdal must be installed first, and a restart might be required after all installation.

• Trajectory Data

  • There are some open-source trajectory datasets, e.g., TDrive.
  • Data should be organized as text files. A text file can contain several trajectories (refer to sample.txt.template)

• Road Network Data

  • OpenStreetMap (OSM) is an open-source map data source
  • The Shapefile road network format can be generated by osm2rn.