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A U-Net Model Leveraging Multiple Remote Sensing Data Sources for Flood Extent Mapping

This repository contains the implementation for a U-Net (Ronneberg et al. 2015) model that leverages multiple remote sensing data for flood extent mapping using the dataset from the FDSI sub-task from the Multimedia Satellite Task of the MediaEval 2017. The presented U-Net leverages a dense connectivity pattern (removing the need for distant layers to re-learn redundant feature maps), and Channel and Spatial Squeeze and Excite blocks (re-calibrating the learned feature maps adaptively).

@article{Ronneberger2015UNetCN,
  title   = {U-Net: Convolutional Networks for Biomedical Image Segmentation},
  author  = {Olaf Ronneberger and Philipp Fischer and Thomas Brox},
  journal = {ArXiv},
  year    = {2015},
  volume  = {abs/1505.04597}
}

Files needed not in this repository

The dataset files are too big to put on a github repository, so it is necessary to download them from this Google Drive folder and place them in the following hierarchy:

project
│   README.md
│   main.py
│   ...
└─── flood-data
│   │    devset_01_elevation_and_slope
│   │    devset_01_imperviousness
│   │    devset_01_NDVI
│   │    devset_01_NDWI
│   │    devset_01_satellite_images
│   │    devset_01_segmentation_masks
│   │    ... 

How to use

The code was developed and tested in Python 3.6.7 with Keras 2.2.4, using Tensorflow 1.13.2 as backend. The code supports re-training and model loading from a previous saved model. To run the script simply execute:

$ python3 main.py --mode {train, load}  --channels {three, four, six, seven, eight} 

Acknowledgments