Built for
City Planning

Satellite/Aerial Image Segmentation

Project Highlights

To find the different segments in satellite image (i.e.buildings, roads, trees, crops and water) for smart city planning

Smart city planning

Our Solution

We have developed a Deep Neural Network by using the following data

  • The dataset consists of 8-band commercial grade satellite imagery taken from Space Net dataset.
  • Train collection contains few tiff files for each of the 24 locations.
  • Every location has an 8-channel image containing spectral information of several wavelength channels (red, red edge, coastal, blue, green, yellow, near-IR1 and near-IR2). These files are located in data/mband/ directory.
  • Also available are correctly segmented images of each training location, called mask. These files contain information about 5 different classes: buildings, roads, trees, crops and water (note that the original Kaggle contest had 10 classes).
  • Resolution for satellite images is 16-bit. However, mask-files are 8-bit.

Smart city development

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