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