In this project, we propose a method to label roads in high-resolution aerial images on a per-pixel basis. With today's influx of satellite data, manual labeling has become infeasible and we need to rely on Computer Vision for processing. Our approach uses a U-Net, a fully convolutional neural network which was introduced in 2015 for biomedical image segmentation. Since neural networks require large amounts of training data, we have devised a method to automatically generate labeled training data from Google Maps. Such generated data is not as good qualitatively as human-annotated images, but it is available in almost unlimited amounts.
Use the package manager pip to install dependencies.
pip install -r requirements.txt-
baseline1_model.py: Run this python file to create a submission file from the first baseline model directly. -
baseline2_model_pipeline.ipynb: Run full pipeline of baseline 2 model with visuals. -
final_model_eval.ipynb: Run eval of pretrained final model with visuals. -
training_final_model.py: Run only the training of the final model (for cluster). -
The Automatic data generator comes as seperate project called
Mapscrape
- Run the
training_final_model.pyon the Leonhard Cluster with at least 36 GB of RAM or use the pretrained model inModels\final_model.h5 - Import your self-trained model into
Models\final_model.h5and locally finish the pipeline onfinal_model_eval.ipynb.
Jason Friedman, Anna Laura John, Renato Menta, Dominic Weibel