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Deep Learning Based Damage Detection on Post-Hurricane Satellite Imagery

This repo contains the materials presented in the paper "Deep Learning Based Damage Detection on Post-Hurricane Satellite Imagery" submitted to the INFORMS 2018 Best Student Paper Competition.

The repo contains the following items:

  • Source python code for image classification
  • Source python code for extract, filter, crop the bounding boxes from raw satellite imagery
  • Source code to download raw satellite imagery from Digital Globe website
  • Source code to manipulate geoTIFF files using gdal library
  • The short paper in PDF
  • The poster presentation in PDF
  • The dataset:
    1. train_another : the training data; 5000 images of each class
    2. validation_another: the validation data; 1000 images of each class
    3. test_another : the unbalanced test data; 8000/1000 images of damaged/undamaged classes
    4. test : the balanced test data; 1000 images of each class

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