3 citations · 3 across the 4 of their papers we have counts for
4 papers
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…