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Waterbody Detection Via Deep Learning

This project explores the application of deep learning to waterbody detection.

Dataset

https://drive.google.com/file/d/1faVYayxdNFGx2m0IxswDncoKmIxusdf7/view

Running The Script

To run the script, simply execute main.py with the index of the GPU you want to train with specified as an optional parameter. If no GPU is specified, the script will default to GPU 0.

python3 main.py [GPU]

Configuration

The script expects an external file called config.json in which the use should specify the desired configuration. Below is an example of such a file and a table outlining the effects of each setting.

Example Configuration

{
  "timestamp": 1,
  "patch_size": 512,
  "experiment_tag": "unet_multispectral",
  "create_logs": true,
  "train": true,
  "test": true,
  "experiments": 1,
  "use_mixed_precision": true,
  "hyperparameters": {
    "model": "unet", 
    "bands": ["RGB", "NIR", "SWIR"],
    "backbone": null,
    "learning_rate": 0.00005,
    "fusion_head": "naive",
    "loss": "jaccard_bce",
    "batch_size": 4,
    "epochs": 50,
    "apply_transfer": false,
    "random_subsample": false, 
    "water_threshold": 0
  }
}

Available Settings

Setting Effects
timestamp The timestamp to use (1, 2 or 3)
patch_size The desired size of the generated patches
experiment_tag The human-readable tag with which to lable the experiment
create_logs Indicates whether or not we want to create logs for the experiment
train Whether or not we want to run the training loop
test Whether or not we want to test the trained model on the test set
experiments Indicate the number of identical experiments we want to run
use_mixed_precision Indicate the number of identical experiments we want to run

Available Hyperparameters

Hyperparameter Effects
model The model we want to use
bands The bands used as inpiut to the model
backbone The model of the pre-trained backbone we want to use
learning_rate The learning rate used by the optimizer
fusion_head The type of fusion head to use to combine spectral bands
loss The loss to use during training
batch_size The size of batches used in training
epochs The number of epochs to train for
apply_transfer Whether or not to apply the PCT water transfer method
random_subsample Whether or not to randomly sample patches for training
water_threshold The threshold at which to stop transplanting water bodies if apply_transfer=true

Citation

Please cite our work if it is helpful for your research.

@article{rs15051253,
title={Water Body Extraction from Sentinel-2 Imagery with Deep Convolutional Networks and Pixelwise Category Transplantation},
author={Billson, Joshua and Islam, MD Samiul and Sun, Xinyao and Cheng, Irene},
journal={Remote Sensing},
volume={15},
year={2023},
number={5},
article-number={1253},
url={https://www.mdpi.com/2072-4292/15/5/1253},
issn={2072-4292},
doi={10.3390/rs15051253}
}