Geographically-Weighted Weakly Supervised Bayesian High-Resolution Transformer for 200m Resolution Pan-Arctic Sea Ice Concentration Mapping and Uncertainty Estimation using Sentinel-1, RCM, and AMSR2 Data

2026-03-03

AI Summary

This study addresses the challenges of high-resolution Arctic sea ice concentration mapping, which are hindered by subtle ice features, label noise, model uncertainty, and heterogeneity among multi-source remote sensing data. To overcome these limitations, the authors propose a high-resolution Vision Transformer model that integrates Sentinel-1, RCM, and AMSR2 observations. The approach innovatively combines geographically weighted weakly supervised learning, Bayesian neural networks, and a decision-level multi-source fusion strategy to produce pan-Arctic sea ice concentration maps at 200-meter resolution with quantified uncertainty. Experimental results demonstrate that, during the minimum sea ice extents in 2021 and 2025, the model achieves a feature detection accuracy of 0.70 on Sentinel-1 data and attains a strong correlation (R² = 0.90) with the ARTIST sea ice concentration product.

Technology Category

• Computer Vision: Remote Sensing / Geospatial AI
• Machine Learning: Multi-class/Multi-label Learning & Extreme Classification
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Although high-resolution mapping of pan-Arctic sea ice with reliable corresponding uncertainty is essential for operational sea ice concentration (SIC) charting, it is a difficult task due to key challenges, such as the subtle nature of ice signature features, inexact SIC labels, model uncertainty, and data heterogeneity. This study presents a novel Bayesian High-Resolution Transformer approach for 200 meter resolution pan-Arctic SIC mapping and uncertainty quantification using Sentinel-1, RADARSAT Constellation Mission (RCM), and Advanced Microwave Scanning Radiometer 2 (AMSR2) data. First, to improve small and subtle sea ice feature (e.g., cracks/leads, ponds, and ice floes) extraction, we design a novel high-resolution Transformer model with both global and local modules that can better discern the subtle differences in sea ice patterns. Second, to address low-resolution and inexact SIC labels, we design a geographically-weighted weakly supervised loss function to supervise the model at region level instead of pixel level, and to prioritize pure open water and ice pack signatures while mitigating the impact of ambiguity in the marginal ice zone (MIZ). Third, to improve uncertainty quantification, we design a Bayesian extension of the proposed Transformer model, treating its parameters as random variables to more effectively capture uncertainties. Fourth, to address data heterogeneity, we fuse three different data types (Sentinel-1, RCM, and AMSR2) at decision-level to improve both SIC mapping and uncertainty quantification. The proposed approach is evaluated under pan-Arctic minimum-extent conditions in 2021 and 2025. Results demonstrate that the proposed model achieves 0.70 overall feature detection accuracy using Sentinel-1 data, while also preserving pan-Arctic SIC patterns (Sentinel-1 R\textsuperscript{2} = 0.90 relative to the ARTIST Sea Ice product).

Problem

• sea ice concentration
• high-resolution mapping
• uncertainty estimation
• weakly supervised learning
• data heterogeneity

Innovation

• Bayesian Transformer
• Weakly Supervised Learning
• Geographically-Weighted Loss
• High-Resolution Sea Ice Mapping
• Multi-Sensor Fusion