Prerequisites

WARNING: this is likely an incomplete guide, do not hesitate to create an issue or pull request if something is not working as expected
Python 3.7 + geopandas, matplotlib, numpy, pytorch, requests, scikit-image

Data annotation

define a bounding box in the annotate.py script in annotation/
starting the script will download all assets, i.e., aerial photographs, intersecting with the defined bounding box
it will then display one of the images divided into four patches and you can select all patches containing the target class via keyboard (default keys: 'u', 'i', 'j','k')
this procedure is repeated for every selected patch and for all assets; patches below a certain size are stored as positive examples
after generating positive examples this way, run generate_negative_examples.py to generate a number of examples randomly picked from all assets; manually review these to make sure they do not contain the target class and run the script again if you deleted a few

after generating the dataset, you can train a classifier to distinguish positive from negative examples
go to the classification directory and run train.py after possibly adjusting hyperparameters such as the learning rate or modifying the classifier architecture (classifier.py)
after training is finished, you can check the accuracy of your classifier with the test_overview.py script; this will load an asset and display the regions of interest detected by your classifier; if you have manually annotated the asset it will also show your annotations
you can use this overview to select patches that were missclassified (right click on a patch) or select patches with the target class which you have not selected yet (left click on patch); the former will be stored in a separate directory, while the latter will be added to the positive examples you have generated during the "Data annotation" phase
you can check the precision/recall performance of the classifier by running test_precision_recall.py; this gives you a chance to tune the DETECTION_THRESHOLD; this threshold defines which patch is considered a match to the target class based on the output of the classifier
when you're happy with your classifier's performance you can run export_ROIs.py to apply your trained classifier to all assets locally available and export the coordinates of each patch that has been classified as containing the target class in GeoJSON format

to display your results on the map, you can first merge intersecting patches with accumulate_ROIs.py in conversion/ and then export them as a KML file with export_kml.py