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Example: detection of quarries

A sample working setup is provided here, enabling the end-user to detect quarries (mineral extraction sites) in Switzerland over several years.
It consists of the following elements:

  • ready-to-use configuration files:
    • config_trne.yaml;
    • config_det.yaml;
    • detectron2_config_dqry.yaml.
  • Input data in the data subfolder:
    • quarry labels issued from the swissTLM3D product, revised and synchronized with the 2020 SWISSIMAGE orthophotos;
    • the delimitation of the Area of Interest (AoI);
    • the Swiss DEM raster is too large to be saved on this platform but can be downloaded from this link using the EPSG:4326 coordinate reference system. The raster must be re-projected to EPSG:2056, renamed as switzerland_dem_EPSG2056.tif and located in the DEM subfolder. This procedure is managed by running the bash script get_dem.sh.
  • A data preparation script (prepare_data.py) producing the files to be used as input to the generate_tilesets stage.
  • A post-processing script (filter_detections.py) which filters detections according to their confidence score, altitude and area. The script also identifies and merges groups of nearby polygons.

The workflow can be run end-to-end by issuing the following list of commands, from the root folder of this GitHub repository:

$ sudo chown -R 65534:65534 examples
$ docker compose run --rm -it stdl-objdet
nobody@<id>:/app# cd examples/quarry-detection
nobody@<id>:/app# python prepare_data.py config_trne.yaml
nobody@<id>:/app# stdl-objdet generate_tilesets config_trne.yaml
nobody@<id>:/app# stdl-objdet train_model config_trne.yaml
nobody@<id>:/app# stdl-objdet make_detections config_trne.yaml
nobody@<id>:/app# stdl-objdet assess_detections config_trne.yaml
nobody@<id>:/app# python prepare_data.py config_det.yaml
nobody@<id>:/app# stdl-objdet generate_tilesets config_det.yaml
nobody@<id>:/app# stdl-objdet make_detections config_det.yaml
nobody@<id>:/app# bash get_dem.sh
nobody@<id>:/app# python filter_detections.py config_det.yaml
nobody@<id>:/app# exit
$ sudo chmod -R a+w examples

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

The model is trained on the 2020 SWISSIMAGE mosaic. Inference can be performed on SWISSIMAGE mosaics of the product SWISSIMAGE time travel by changing the year in config_det.yaml. It should be noted that the model has been trained on RGB images and might not perform as well on black and white images.

For more information about this project, see this repository.

Disclaimer

Depending on the end purpose, we strongly recommend users not to take for granted the detections obtained through this code. Indeed, results can exhibit false positives and false negatives, as is the case in all machine learning-based approaches.