Dataset for “A Hausdorff-Guided Deep Learning Approach for Monitoring the Motion of Rotating Arctic Ice Floes”
Description
Data for manuscript submitted to GIScience & Remote Sensing entitled “A Hausdorff-Guided Deep Learning Approach for Monitoring the Motion of Rotating Arctic Ice Floes.” The dataset contains training data used for the deep learning framework developed in the study. The data consist of multi-temporal Arctic sea ice images used to train and evaluate the proposed Hausdorff-guided method for monitoring the motion and rotation of Arctic ice floes. Each data folder includes the input ice floe images used for model training in the manuscript. The dataset supports the development and validation of the deep learning model used to detect feature points and estimate the motion of rotating ice floes under complex Arctic marginal ice zone conditions.
Files
Steps to reproduce
The Sentinel-2 Level-2 surface reflectance products provided by the European Space Agency (ESA) serve as the primary data source for extracting ice floe distribution and motion information. Only clear-sky imagery with minimal cloud cover is selected to ensure data quality. True-colour composites are generated using Band 2 (blue, 458–523 nm), Band 3 (green, 543–578 nm), and Band 4 (red, 650–680 nm), each with a spatial resolution of 10 m. To enable accurate area calculations, the projected coordinate system is converted from WGS 1984 UTM Zone 47N to the North Pole Lambert azimuthal equal-area projection. Ice–water classification is performed using a dynamic threshold determined by the local minimum between the two peaks in the grayscale histogram of each image, resulting in a binary mask where white pixels represent ice and black pixels represent water. Small or spurious ice floes with blurred boundaries are removed through a morphological “erosion–marking–dilation” procedure, yielding independent ice floes for further analysis. For deep learning model training, 226 representative ice floe images acquired between 2017 and 2023 are selected. Paired images are generated via affine and perspective transformations, and pseudo-labels are created using homographic adaptation, following a self-supervised strategy. The SuperPoint model is trained with the Adam optimizer (initial learning rate = 1×10⁻⁴, batch size = 8) for 20,000 iterations, with checkpoints saved every 2,000 iterations. The SuperGlue model is subsequently trained end‑to‑end using the transferred SuperPoint weights, also with Adam (initial learning rate = 1×10⁻⁴, decay factor = 0.1 every 10 epochs) for up to 500 epochs (batch size = 4), employing early stopping if the validation loss does not improve for 50 consecutive epochs. All experiments are conducted on a Windows 10 machine equipped with an NVIDIA RTX 4060 GPU (8 GB memory) using CUDA 11.8 and Python 3.11.5. Keypoint extraction and feature matching are performed with a keypoint threshold of 0.001 and a match threshold of 0.1. To facilitate reproducibility, the complete code and trained models will be made publicly available at the URL provided in the paper.
Institutions
- Northwest Institute of Eco-Environment and ResourcesGansu, Lanzhou