DOI: 10.5281/zenodo.15083264
title: A 6-year circum-Antarctic icebergs dataset (2018-2023)

description: What's this?

This is a dataset providing annual iceberg vector distributions from October 2018 to October 2023, extracted from Sentinel-1 SAR imagery covering the region south of 55°S. It spans a continuous six-year time series (2018–2023) with a minimum detectable iceberg area of approximately 0.04 km². The dataset includes vector outlines of icebergs, geometric parameters (area, perimeter, major and minor axes), geographic coordinates, mass estimates based on average thickness and density, and associated uncertainties in both area and mass.

A semi-automated machine learning method (incremental random forest classification) was used to achieve high-precision iceberg detection on large-scale SAR imagery, supplemented by manual interactive correction steps to minimize misclassifications and omissions. Compared to existing low-resolution or regionally focused iceberg products, this dataset offers broader spatial coverage (encompassing the entire Southern Ocean) and an annual temporal resolution, thereby providing essential support for research on the impacts of Antarctic icebergs on the global climate system and the Southern Ocean environment.

This is a "living" dataset, meaning it will be continuously updated and expanded as new data becomes available.

Data Processing Methods Summary



Data Acquisition:Sentinel-1 SAR (Extra Wide mode, HH polarization) images from October of each year between 2018 and 2023 are acquired on the Google Earth Engine (GEE) platform. The region south of 55°S is divided into several 5°×5° tiles, which are then mosaicked

Image Segmentation:After denoising with the Total Variation (TV) algorithm, the SAR images are segmented using the SLIC superpixel method to obtain superpixels with relatively consistent backscatter characteristics. A superpixel is defined as a small, contiguous cluster of adjacent pixels that share similar backscatter characteristics, effectively representing a meaningful image region rather than individual pixels.

Iceberg Detection:For each superpixel, statistical features, histogram features, and texture features (a total of 24 features) are extracted. An incremental random forest classifier is then used for initial classification, and further post-processing and correction are performed interactively in ArcMap 10.8.

Attribute Calculation:For the final detected iceberg vector outlines, their areas, perimeters, and the lengths of the major and minor axes are calculated. Additionally, iceberg mass is estimated based on the buoyancy equilibrium hypothesis (assuming an average iceberg thickness of approximately 232 m and an ice density of about 850 kg/m³), and the uncertainty in iceberg area is quantified.


Data Applications and Significance



Provides critical foundational data for studying the distribution patterns of Antarctic icebergs, their impact on the Southern Ocean environment, and their relationship with global climate change.

This dataset can serve as initial conditions for iceberg dynamics models or coupled ocean-iceberg models, and be used to investigate the effects of iceberg melting on ocean circulation, temperature-salinity structures, and ecosystems.
description: Iceberg detection code

This folder contains all the necessary steps and code for the complete Antarctic iceberg detection and attribute extraction workflow, starting from downloading SAR data on Google Earth Engine (step1 GEE Data Download), through image segmentation (step2 Image segmentation), feature extraction (step3 Feature extraction), incremental random forest classification (step4 Incremental RF classification), and finally, iceberg attribute calculation (step5 attribute_extraction). In addition, there is a demo folder providing example scripts or process demonstrations.

Iceberg sample set

















This file encompasses all the sample points for the annual incremental random forest classification, including the training set, validation set, and test set.