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License: MIT

AI4ER Guided Team Challenge 2023:
Sea Ice Classification

This repository contains code for the Sea Ice Classification Challenge from the 2022-23 cohort of AI4ER.

AI4ER is the the UKRI Centre for Doctoral Training (CDT) in the Application of Artificial Intelligence to the study of Environmental Risks at the University of Cambridge.

Project Description

This goal of this 3-month project was to automatically classify sea ice concentration in the East Weddell Sea, Antarctica. The Weddell Sea is an active area of iceberg calving [1] and a critical shipping route for access to the Halley Research Station, a research facility operated by the British Antarctic Survey. Accurate assessments of sea ice concenration in the East Weddell Sea are hence of great importance to ensure the safety and success of future expeditions.

Data

This project uses two publicly available datasets:

Region of Interest

Relative locations and areal extents of (b) training, (c) validating, and (d) testing subsets.

Temporal and geographical coverage of the dataset.

Pair Number Subset Date of Sea Ice Concentration Chart Observation Window of SAR signals Geographical Coverage
1 training Nov 6, 2017 10:25:50 PM - 10:26:55 PM Weddell Sea
2 training Dec 23, 2017 10:42:00 PM - 10:43:04 PM Weddell Sea
3 training Jan 4, 2018 10:41:59 PM - 10:43:03 PM Weddell Sea
4 training Feb 22, 2018 11:22:34 PM - 11:23:38 PM Weddell Sea
5 training Feb 23, 2018 12:59:54 AM - 1:00:58 AM Weddell Sea
6 training Feb 26, 2018 1:24:13 AM - 1:25:17 AM Weddell Sea
7 training Dec 3, 2018 8:18:15 AM - 8:19:14 AM Antarctica Peninsula
8 training Dec 9, 2018 8:51:00 AM - 8:52:08 AM Antarctica Peninsula
9 training Dec 10, 2018 11:50:06 PM - 11:51:05 PM Antarctica Peninsula
10 training Dec 20, 2018 8:19:15 AM - 8:20:19 AM Antarctica Peninsula
11 training Mar 5, 2020 12:35:47 AM - 12:36:52 AM Weddell Sea
12 training Mar 13, 2020 1:08:15 AM - 1:09:20 AM Weddell Sea
13 training Dec 23, 2021 11:14:52 PM - 11:15:43 PM Weddell Sea
14 validating Mar 13, 2019 11:22:41 PM - 11:23:45 PM Weddell Sea
15 validating Jan 17, 2020 10:01:39 PM - 10:02:43 PM Weddell Sea
16 testing Dec 16, 2022 11:23:48 PM - 11:24:53 PM Weddell Sea
17 testing Dec 22, 2022 1:09:23 AM - 1:10:27 AM Weddell Sea
18 testing Jan 12, 2023 8:32:41 PM - 8:33:45 PM Weddell Sea

Sample visualizations of (a) water and ice, (b) HH band, (c) HV band, (d) angle, and (e) HH/HV.

Categories of sea ice concentrations.

Code Description Code Description Code Description
0 Ice Free 35 3/10 to 5/10 ice 78 7/10 to 8/10 ice
1 Open Water ( extless 1/10 ice) 40 4/10 ice 79 7/10 to 9/10 ice
2 Bergy Water 45 4/10 to 5/10 ice 80 8/10 ice
10 1/10 ice 46 4/10 to 6/10 ice 81 8/10 to 10/10
12 1/10 to 2/10 ice 47 4/10 to 7/10 ice 89 8/10 to 9/10 ice
13 1/10 to 3/10 ice 50 5/10 ice 90 9/10 ice
14 1/10 to 4/10 ice 56 5/10 to 6/10 ice 91 9/10 to 10/10 ice, 9+/10 ice
20 2/10 ice 57 5/10 to 7/10 ice 92 10/10 ice
23 2/10 to 3/10 ice 60 6/10 ice 99 Unknown/Undetermined
24 2/10 to 4/10 ice 67 6/10 to 7/10 ice -9 Null Value
30 3/10 ice 68 6/10 to 8/10 ice 100 Full Ice
34 3/10 to 4/10 ice 70 7/10 ice

Schematic illustration of the overlapping tiling technique.

Models

This project uses three models:

  1. A baseline Decision Tree (DT) model
  2. A basic U-Net
  3. A pretrained resnet34 from the segmentation_models_pytorch Python library, which is distributed under the MIT license. License: MIT

Code Structure

|──────tutorial                     <-- Folder containing tutorial CNN notebook
|──────info.md                      <-- Sea ice concentration values
|──────JASMIN.md                    <-- Step-by-step guide
|──────constants.py                 <-- SAR/ice chart pairs, binary/ternary classes
|──────environment.yml              <-- List of Python modules
|──────interesting_images.csv       <-- List of tiles containing all three categories for ternary classification
|──────interesting_images.py        <-- Generate interesting_images.csv
|──────intro_deep_learning.ipynb    <-- Deep learning notebook
|──────metrics.csv                  <-- Lists mean and std dev of all SAR images
|──────metrics.py                   <-- Calculate metrics.csv
|──────metrics_per_pair.csv         <-- Lists mean and std dev for individual SAR image
│──────model.py                     <-- Unet model and evaluation metrics
│──────split.py                     <-- Construct training & validation datasets
│──────test.py                      <-- Test CNN and save output to WANDB
|──────test_scikit.py               <-- Test DT and save output to WANDB
|──────test_slurm_script.py         <-- Test the model on JASMIN
|──────test_slurm_script_scikit.py  <-- Test the DT on JASMIN
|──────tiling.py                    <-- Generate tiles from SAR/ice chart paris
│──────train.py                     <-- Train CNN and save output to WANDB
|──────train_scikit.py              <-- Train DT and save output to WANDB
|──────train_slurm_script.sh        <-- Train the model on JASMIN
|──────train_slurm_script_scikit.sh <-- Train the DT on JASMIN
│──────util.py                      <-- Load data into the model and normalise
|──────util_scikit.py               <-- Load data into the model, normalise and create the training dataset

Workflow

CNN Workflow (U-Net and ResNet34)

Decision Tree Workflow

Usage

To test the code and recreate the results of this project, follow the steps below:

  1. Clone this repository (for the latest version) or retrieve the archived copy from Zenodo

  2. Create and activate the conda environment using conda activate environment.yml, which contains all required Python modules and versions.

  3. To generate ice chart and SAR tile pairs of 256x256 dimensions run: python tiling.py. Tile pairs containing NaN values will be discarded.

  4. Follow the steps in JASMIN.md to train and test the CNN or DT model. Input arguments that were modified for this project include:

    Argument Options Default
    --model unet, resnet34 unet
    --classification_type binary, ternary binary
    --sar_band3 angle, ratio angle

Contributors

Project core members contributed equally to this work:

With special thanks to our advisors for their project guidance and technical support:

  • Madeline Lisaius, AI4ER PhD Student (2021 Cohort), University of Cambridge
  • Jonathan Roberts, AI4ER PhD Student (2021 Cohort), University of Cambridge
  • Martin Rogers, AI-lab, British Antarctic Survey

References

[1] Brunt ice shelf in Antarctica calves giant iceberg (2023) British Antarctic Survey. Available at: https://www.bas.ac.uk/media-post/brunt-ice-shelf-in-antarctica-calves-giant-iceberg/ (Accessed: March 9, 2023).

[2] The European Space Agency (n.d.) FAQ content, FAQ - Sentinel Online - Sentinel Online. Available at: https://sentinel.esa.int/web/sentinel/faq (Accessed: March 17, 2023).

Significant parts of our deep learning code are inspired by / adapted from the following:

https://github.com/ampersandmcd/DeepExtremeMixtureModel

https://github.com/ampersandmcd/COMETFlows

https://github.com/ampersandmcd/CSE803FinalProject