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This repo contains a UNet based deep learning model for identifying roads from aerial images

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Nirzaree/Detecting-Roads-from-Satellite-Images

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Detecting-Roads-from-Satellite-Images

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:

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This repo contains a UNet based deep learning model for identifying roads from aerial images

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