Aiice

---

AIICE is an open-source Python framework designed as a standardized benchmark for spatio-temporal forecasting of Arctic sea ice concentration. It provides reproducible pipelines for loading, preprocessing, and evaluating satellite-derived OSI-SAF data, supporting both short- and long-term prediction horizons

Installation

The simplest way to install framework with pip:

pip install aiice-bench

Quickstart

The AIICE class provides a simple interface for loading Arctic ice data, preparing datasets, and benchmarking PyTorch models:

from aiice import AIICE

# Initialize AIICE with a sliding window 
# of past 30 days and forecast of 7 days
aiice = AIICE(
    pre_history_len=30,
    forecast_len=7,
    batch_size=32,
    start="2022-01-01",
    end="2022-12-31"
)

# Define your PyTorch model
model = MyModel()

# Run benchmarking to compute metrics on the dataset
report = aiice.bench(model)
print(report)

Check package doc and see more usage examples. You can also explore the raw dataset and work with it independently via Hugging Face

Leaderboard

The leaderboard reports the mean performance of each model across the evaluation dataset. You can check models' setup in examples.

		baseline_mean	baseline_repeat	conv2d	conv3d	convlstm
Barents Sea	bin_accuracy	0.874963	0.848936	0.937071	0.891255	0.941710
	iou	0.185126	0.331170	0.647688	0.420801	0.671497
	mae	0.130236	0.151377	0.067575	0.113846	0.057296
	mse	0.053554	0.106431	0.028444	0.064654	0.025509
	psnr	12.712070	9.729317	15.460110	11.894089	15.948077
	rmse	0.231418	0.326238	0.168653	0.254271	0.159578
	ssim	0.540464	0.609196	0.696043	0.618139	0.784737
Chukchi Sea	bin_accuracy	0.656515	0.675528	0.947459	0.789110	0.948085
	iou	0.126601	0.364351	0.865943	0.585862	0.871389
	mae	0.269926	0.300754	0.069100	0.198657	0.061957
	mse	0.124306	0.246038	0.023475	0.125997	0.026552
	psnr	9.055069	6.089983	16.293947	8.996499	15.796962
	rmse	0.352571	0.496022	0.153215	0.354958	0.162596
	ssim	0.405798	0.385161	0.651510	0.449680	0.751346
Kara Sea	bin_accuracy	0.801598	0.797711	0.939550	0.844245	0.945934
	iou	0.282630	0.412451	0.785852	0.559398	0.805982
	mae	0.162785	0.185920	0.065702	0.149524	0.054693
	mse	0.070723	0.136968	0.025262	0.092185	0.022224
	psnr	11.504373	8.633821	15.975368	10.358038	16.550935
	rmse	0.265939	0.370091	0.158939	0.303539	0.148913
	ssim	0.604080	0.590542	0.725831	0.589535	0.810979
Laptev Sea	bin_accuracy	0.839829	0.863018	0.964288	0.897629	0.970806
	iou	0.387533	0.534633	0.859092	0.683309	0.882638
	mae	0.115111	0.122237	0.043340	0.094628	0.030666
	mse	0.051770	0.094377	0.015273	0.066438	0.011886
	psnr	12.859248	10.251351	18.160892	11.784326	19.400338
	rmse	0.227529	0.307208	0.123582	0.257630	0.108099
	ssim	0.782073	0.746823	0.837163	0.802543	0.907448
Sea of Japan	bin_accuracy	0.994356	0.989473	0.994356	0.995731	0.995423
	iou	0.000000	0.035046	0.000000	0.000000	0.383335
	mae	0.013824	0.016332	0.009841	0.008582	0.006749
	mse	0.004467	0.009577	0.005990	0.004908	0.002619
	psnr	23.499943	20.187490	22.225956	22.945065	25.774397
	rmse	0.066835	0.097865	0.077393	0.069567	0.051133
	ssim	0.841847	0.879064	0.922021	0.919443	0.923896