In this notebook we will look at satellite imagery data from Masachusetts and detect roads from the images. The dataset has been obtained from kaggle

The data is organized as follows:

.
└── training
    ├── input
    └── output
├── testing
│   ├── input
│   └── output

where input folder contains the images and output folder contains the corresponding mask images.

• The size of the images is: (length, width, color_channels) = (1500, 1500, 3)

• The two tasks in the analysis are: - Data Pipeline: Preprocessing the raw images to make them ready for the deep learning model - Model Pipeline: Build a deep learning model to identify roads from the images

• Data pipeline does the following:

  • Crop input images from (1500, 1500, 3) into multiple smaller images of size (256, 256, 3)
  • Apply similar operation on the mask images
  • Save images as h5 files.

• Model pipeline does the following:

  • Trains a Unet model on the cropped images
  • Validates the model on test data using defined metrics

Here are some predictions from the model: