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Automatic Segmentation of Sinkholes Using a Convolutional Neural Network

M. Usman Rafique, Nathan Jacobs, Junfeng Zhu

Paper   PDF   Dataset

This repository contains PyTorch code for the following paper:

M. Usman Rafique, Junfeng Zhu, Nathan Jacobs "Automatic Segmentation of Sinkholes Using a Convolutional Neural Network", AGU Earth and Space Science Journal, 2022.

Setting Up

Installation

We recommend using a virtual environment with anaconda. If you don't already have anaconda installed, please visit this page to install anaconda: https://docs.anaconda.com/anaconda/install/index.html

We provide a virtual environment file environment.yml in this repository that can be used to make a suitable virtual environment. In terminal, go to the path of this repo and use this command:

conda env create -f environment.yml

Once you set up the virtual environment (named sinkhole), you can activate it by:

conda activate sinkhole

Dataset

Our dataset has been publicly released at Zenodo:

Please download all the files in a directory named data in this repository. The data is released under the Creative Commons Attribution 4.0 license.

Training

To train a model, first set up the required settings in the config.py file. The default settings use two-channel DEM derivatives as input and a Unit Gaussian normalization. Other inputs and normalization options are available. Additionally, data and optimization settings can also be set in the config file.

To start training, simply use the command:

python3 train.py

At the end of the training, the trained model checkpoint and training logs will be saved in the cfg.train.out_dir specified in the config.py file.

Evaluation

To evaluate a trained model, set the same cfg.train.out_dir in config.py that was used to train the model and run the command:

python3 evaluate.py

Metrics will be displayed in the terminal and saved to the cfg.train.out_dir directory. This script performs a sweep of different thresholds on the val set and saves the best threshold in a file best_threshold.txt as well.

Visualization

To generate qualitative visual results, run the command

python3 visualize.py

As before, it is required that cfg.train.out_dir in config.py specifies a directory that contains a trained model checkpoint.

Acknowledgement

We thank Nicole Wong and Aram Ansary Ogholbake for testing the code.

Citation

If you find this paper or code helpful, please cite our paper: