Skip to content

Latest commit

 

History

History
28 lines (22 loc) · 1.66 KB

README.md

File metadata and controls

28 lines (22 loc) · 1.66 KB
Raw

Example: determination of the type or road surface

A sample working setup is provided here to allow, allowing the end-user to detect roads and determine their type of surface (natural or artificial). This example is illustrating the functioning for the multi-class case.
It is made of the following assets:

  • the read-to-use configuration files
    • config_rs.yaml
    • detectron2_config_3bands.yaml,
  • the initial data in the data subfolder:
    • the roads and forests from the product swissTLM3D
    • the delimitation of the AOI
    • an excel file with the road parameters,
  • a data preparation script (prepare_data.py) producing the files to be used as input to the generate_tilesets.pyscript.

After performing the installation described in the root folder of the project, the end-to-end workflow can be run by issuing the following list of commands, straight from this folder:

$ python prepare_data.py config_rs.yaml
$ stdl-objdet generate_tilesets config_rs.yaml
$ stdl-objdet train_model config_rs.yaml
$ stdl-objdet make_detections config_rs.yaml
$ stdl-objdet assess_detections config_rs.yaml

This example is made up from a subset of the data used in the proj-roadsurf project. For more information about this project, you can consult the associated repository and its full documentation.
The original project does not use the original script for assessment but has his own.

We strongly encourage the end-user to review the provided config_rs.yaml file as well as the various output files, a list of which is printed by each script before exiting.