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The Arctic Retrogressive Thaw Slumps Data Set (ARTS)

This repository hosts:

  1. The ARTS main data set (current and historical versions)
  2. Tutorials (Jupyter Notebook or Rmarkdown) for preparing and formatting contributions to the ARTS data set (ARTS/Tutorial/data_formatting.ipynb or ARTS/Tutorial/data_formatting.Rmd)
  3. Tutorial for splitting the ARTS data set into training, validation, and testing subsets for machine learning models while ensuring no data leakage (ARTS/Tutorial/Automated_Training_Validation_Testing_Data_Split.ipynb)

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Instructions for Use

Use the DOI link: DOI

Select Versions from the right side panel and download the zip file to your local machine.

Steps for contributing to the ARTS data set

1. Fork this Repository and Clone Your Fork Onto Your Local Machine

Fork this repository using the Fork button in the top right corner of the browser. Next, clone your forked repository. From the forked repository on your GitHub page, copy the URL using the Code button in the top right of the browser. From the command line (whichever shell you use for git), navigate to the directory in which you would like to clone the repository (this should be the directory in which you would like to work on this project), and run: git clone {URL}.

2. Set Up a Conda Environment from env.yml

Make sure you have Anaconda or Miniconda installed. Mamba is also recommended (mamba is not required, but is much faster than conda).

In the command line, make sure you are in the repository directory and run: mamba env create -f env.yml (or conda env create -f env.yml, if you don't have mamba installed). This will create a conda environment named rts_dataset. Run: conda env list to ensure that rts_dataset shows up.

3. Run the Scripts (Choose either Rmarkdown or Jupyter Notebook)

Rmarkdown is preferred, as the step to check for intersections is much faster in R (~40x faster as of v.0.0.20-alpha). (If you have suggestions for speeding up the Python version, please be in touch.)

Copy your new, pre-formatted RTS file into the input_data folder. Take a look at input_data/metadata_description for formatting requirements.

If using Python, ensure you are in the repository directory and activate the conda environment by running: conda activate rts_dataset. Open Tutorial/rts_dataset_formatting.ipynb in either Jupyter Notebook or Jupyter Lab by running: jupyter notebook or jupyter lab. Follow the instructions in the script to format your data set.

If using R, open the project, ARTS.Rproj, in RStudio. Open Tutorial/rts_dataset_formatting.Rmd. Install any missing packages required to run the script. Follow the instructions in the script to format your data set. If you encounter issues during uuid generation, make sure that rts_dataset is specified as the Python interpreter in the Python section of project options (Tools > Project Options > Python).

Metadata Formatting Summary Example:

FieldName Format Required Description
CentroidLat Decimal Degrees TRUE Polygon centroid latitude in EPSG:4326
CentroidLon Decimal Degrees TRUE Polygon centroid longitude in EPSG:4326
RegionName String TRUE Name of the geographical region
CreatorLab String TRUE Data creator and associated organization
BaseMapDate String TRUE Date of base map used for RTS delineation in YYYY-MM-DD for a single date, range of dates should be separated by a comma
BaseMapSource String TRUE Name of the satellite sensor used for RTS digitisation
BaseMapResolution Number TRUE Resolution of the imagery used for RTS digitisation (meters)
TrainClass String TRUE 'Positive' for genuine RTS and 'Negative' for background
LabelType String TRUE Type of digitisation, e.g. 'Polygon', 'BoundingBox'
MergedRTS String auto-generated UIDs of intersecting RTS that merged into one RTS
StabilizedRTS String auto-generated UIDs of intersecting stabilized RTS scars
ContributionDate String auto-generated Date of contribution to the ARTS main file in YYYY-MM-DD
UID 36-character alphanumeric string auto-generated Unique identifier generated using uuid5 by concatenating all 'Required-True' fields as a single string

Related Publications

Yang Y, Rodenhizer H, Rogers B M, et al. ARTS: a scalable data set for Arctic Retrogressive Thaw Slumps[R]. Copernicus Meetings, 2024. (https://meetingorganizer.copernicus.org/EGU24/EGU24-1365.html)

Yili Yang, Heidi Rodenhizer, & Jacqueline Dean. (2024). Arctic Retrogressive Thaw Slumps (ARTS): digitisations of pan-Arctic retrogressive thaw slumps, 1985-2021. Arctic Data Center. doi:10.18739/A2PK0738B.

Selected Data Examples

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