6 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…
An Autonomous GeoAI Agent for Arctic Eco-Navigation
Samira Alkaee Taleghan, Younghyun Koo, Farnoush Banaei-Kashani
Arctic maritime navigation is becoming increasingly important as changing sea-ice conditions expand seasonal accessibility while simultaneously introducing substantial operational,…
Optimal Planning and Machine Learning for Responsive Tracking and Enhanced Forecasting of Wildfires using a Spacecraft Constellation
Sreeja Roy-Singh, Vinay Ravindra, Richard Levinson +8
We propose a novel concept of operations using optimal planning methods and machine learning (ML) to collect spaceborne data that is unprecedented for monitoring wildfires, process…
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…