For this choice of project task, we provide a set of satellite images acquired from GoogleMaps. We also provide ground-truth images where each pixel is labeled as road or background.
Your task is to train a classifier to segment roads in these images, i.e.
assigns a label road=1, background=0 to each pixel.
Submission system environment setup:
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The dataset is available from the CrowdAI page.
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Obtain the python notebook
segment_aerial_images.ipynbfrom this github folder, to see example code on how to extract the images as well as corresponding labels of each pixel.
The notebook shows how to use scikit learn to generate features from each
pixel, and finally train a linear classifier to predict whether each pixel is
road or background. Or you can use your own code as well. Our example code here
also provides helper functions to visualize the images, labels and predictions.
In particular, the two functions mask_to_submission.py and
submission_to_mask.py help you to convert from the submission format to a
visualization, and vice versa.
- As a more advanced approach, try
tf_aerial_images.py, which demonstrates the use of a basic convolutional neural network in TensorFlow for the same prediction task.
Evaluation Metric: F1 score