This repository contains the submission for the student project in the "Computational Intelligence Lab" course at ETH Zürich in spring 2017. The goal of the projects was to find a novel solution to a satellite imagery segmentation problem.

• Florian Chlan
• Samuel Kessler
• Yu-chen Tsai

Can be found here

This repo was cloned from https://github.com/mato93/road-extraction-from-aerial-images.

• Python 3.5.3+
• Tensorflow 1.1.0
• Numpy
• scikit-learn
• scikit-image
• Pillow
• Matplotlib
• Tqdm
• scipy

1. Create the ./data/ directory.
2. Add the unzipped kaggle training and test_set to the ./data/ directory.
3. Run the following commands:

bash setup.sh
cd src
python3 run.py

The result of running the above will create two submission csv files. submission_cae_patchsize24.csv includes the final results of the median frequency class balancing (MFCB) CNN and the denoising convolutional autoencoder, and the submission_no_postprocessing.csv contains the predictions of the MFCB CNN only.

|-- src
  |-- baseline
    |-- model_baseline1.py: defines the first baseline for the project. Provided by CIL TAs.
    |-- model_baseline2.py: defines the second baseline. Provided by previous group, cloned from here.
  |-- constants_baseline2.py: defines the constants used for second baseline. Provided by previous group, cloned from here.
  |-- data_loading_module.py: helper functions for baseline 2 and CNN with weighted loss. Provided by previous group, cloned from here.
  |-- patch_extraction_module.py: helper functions for baseline 2 and CNN with weighted loss. Provided by previous group, cloned from here.
  |-- model_weightedloss.py: defines the median frequency class balancing CNN. Adapted from previous group.
  |-- median_frequency_balancing.py: helper function for the median frequcny class balancing CNN.
  |-- constants.py: config for the median frequency class balancing CNN.
  |-- autoencoder
    |-- model.py: defines the model of the autoencoder.
    |-- ae_config.py: defines the constants for the autoencoder.
    |-- denoise_autoencoder.py: runs the autoencoder.
  |-- cnn_autoencoder
    |-- model.py: defines the convolutional autoencoder model.
    |-- cnn_ae_config.py: defines the config for the convolutional autoencoder.
    |-- denoise_cnn_autoencoder.py: runs the convolutional autoencoder.
  |-- mask_to_submission.py: Converts test images to submission csv file. Provided by CIL TAs.
|-- notebooks
   |-- ImgCorruptionSandbox.ipynb: Notebook which demonstrates the corruption process for the denoising autoencoders.