14 citations · 17 across the 6 of their papers we have counts for
8 papers
Multi-Label Proportion Learning for Sea-Ice Type Prediction
Samira Alkaee Taleghan, Younghyun Koo, Andrew P. Barrett +1
Sea-ice type prediction is important for climate monitoring, maritime navigation, and decision-making in polar regions. The main source of label data for this task is the ice chart…
Uncertainty-Aware Learning from Multi-Expert Interval Targets
Samira Alkaee Taleghan, Younghyun Koo, Andrew P. Barrett +1
Many machine learning (ML) applications rely on expert labels, and qualified experts may provide different but plausible interpretations of the same observation. Such variation acr…
Uncertainty-Aware Sea-Ice Type Mapping with Multiple Ice Charts
Samira Alkaee Taleghan, Younghyun Koo, Andrew P. Barrett +1
Sea-ice stage of development (SoD) describes the age and associated thickness of sea ice and provides important information for navigation, and operational ice monitoring. SoD labe…
Investigating the Effect of Spatial Context on Multi-Task Sea Ice Segmentation
Behzad Vahedi, Rafael Pires de Lima, Sepideh Jalayer +3
Capturing spatial context at multiple scales is crucial for deep learning-based sea ice segmentation. However, the optimal specification of spatial context based on observation res…
IceBench: A Benchmark for Deep Learning based Sea Ice Type Classification
Samira Alkaee Taleghan, Andrew P. Barrett, Walter N. Meier +1
Sea ice plays a critical role in the global climate system and maritime operations, making timely and accurate classification essential. However, traditional manual methods are tim…
Ice-FMBench: A Foundation Model Benchmark for Sea Ice Type Segmentation
Samira Alkaee Taleghan, Morteza Karimzadeh, Andrew P. Barrett +2
Accurate segmentation and mapping of sea ice types is crucial for safe polar navigation, offshore operations, and climate monitoring. While deep learning has demonstrated strong po…
Partial Label Learning with Focal Loss for Sea Ice Classification Based on Ice Charts
Behzad Vahedi, Benjamin Lucas, Farnoush Banaei-Kashani +4
Sea ice, crucial to the Arctic and Earth's climate, requires consistent monitoring and high-resolution mapping. Manual sea ice mapping, however, is time-consuming and subjective, p…
Enhancing sea ice segmentation in Sentinel-1 images with atrous convolutions
Rafael Pires de Lima, Behzad Vahedi, Nick Hughes +3
Due to the growing volume of remote sensing data and the low latency required for safe marine navigation, machine learning (ML) algorithms are being developed to accelerate sea ice…