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DeepGlobe Land Cover Classification Challenge

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DATASET

DATA

Label

Evaluation Metric

Result

<table border=0> <tr> <td> <img src="/img/6399_sat.jpg" border=0 margin=1 width=512> </td> <td> <img src="/img/6399_mask.png" border=0 margin=1 width=512> </td> </tr> </table>

Acknowledgment

This repo borrows code heavily from

Cite us

@INPROCEEDINGS{9064236,  
author={Y. {Li} and L. {Chen}},  
booktitle={2019 IEEE 5th International Conference on Computer and Communications (ICCC)},   
title={Land Cover Classification for High Resolution Remote Sensing Images with Atrous Convolution and BFS},   year={2019},  
volume={},  
number={},  
pages={1808-1813},}

How to implement this repo?

File Structure

Code functions and excution order

  1. rgb2label.py the satellite images id_mask.png are RGB images. This code change the RGB images to onechannel images so we can use them to generate tfrecord files.

  2. create_tf_record_all.py generate tfrecord files. The input and output directory were set in the code follows.

    parser.add_argument('--data_dir', type=str, default='./dataset/',
                        help='Path to the directory containing the PASCAL VOC data.')
    
    parser.add_argument('--output_path', type=str, default='./dataset',
                        help='Path to the directory to create TFRecords outputs.')
    
    parser.add_argument('--image_data_dir', type=str, default='land_train',
                        help='The directory containing the image data.')
    
    parser.add_argument('--label_data_dir', type=str, default='onechannel_label',
                        help='The directory containing the augmented label data.')
    
  3. train.py

  4. inference.py To apply semantic segmentation to your images.

Other useful codes

  1. utils a toolkit

  2. deeplab_model.py the deeplabV3+ model

  3. tensorboard

    tensorboard --logdir MODEL_DIR
    

    If you want to run Tensorboard on a remote server. This stackoverflow discussion may be help