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 time-consuming, costly, and have inherent biases. Automating sea ice type classification addresses these challenges by enabling faster, more consistent, and scalable analysis. While both traditional and deep learning approaches have been explored, deep learning models offer a promising direction for improving efficiency and consistency in sea ice classification. However, the absence of a standardized benchmark and comparative study prevents a clear consensus on the best-performing models. To bridge this gap, we introduce \textit{IceBench}, a comprehensive benchmarking framework for sea ice type classification. Our key contributions are threefold: First, we establish the IceBench benchmarking framework which leverages the existing AI4Arctic Sea Ice Challenge dataset as a standardized dataset, incorporates a comprehensive set of evaluation metrics, and includes representative models from the entire spectrum of sea ice type classification methods categorized in two distinct groups, namely, pixel-based classification methods and patch-based classification methods. IceBench is open-source and allows for convenient integration and evaluation of other sea ice type classification methods; hence, facilitating comparative evaluation of new methods and improving reproducibility in the field. Second, we conduct an in-depth comparative study on representative models to assess their strengths and limitations, providing insights for both practitioners and researchers. Third, we leverage IceBench for systematic experiments addressing key research questions on model transferability across seasons (time) and locations (space), data downscaling, and preprocessing strategies.

The increase in Arctic marine activity due to rapid warming and significant sea ice loss necessitates highly reliable, short-term sea ice forecasts to ensure maritime safety and operational efficiency. In this work, we present a novel data-driven approach for sea ice condition forecasting in the Gulf of Ob, leveraging sequences of radar images from Sentinel-1, weather observations, and GLORYS forecasts. Our approach integrates advanced video prediction models, originally developed for vision tasks, with domain-specific data preprocessing and augmentation techniques tailored to the unique challenges of Arctic sea ice dynamics. Central to our methodology is the use of uncertainty quantification to assess the reliability of predictions, ensuring robust decision-making in safety-critical applications. Furthermore, we propose a confidence-based model mixture mechanism that enhances forecast accuracy and model robustness, crucial for reliable operations in volatile Arctic environments. Our results demonstrate substantial improvements over baseline approaches, underscoring the importance of uncertainty quantification and specialized data handling for effective and safe operations and reliable forecasting.

Animal-borne sensors ('bio-loggers') can record a suite of kinematic and environmental data, which can elucidate animal ecophysiology and improve conservation efforts. Machine learning techniques are useful for interpreting the large amounts of data recorded by bio-loggers, but there exists no standard for comparing the different machine learning techniques in this domain. To address this, we present the Bio-logger Ethogram Benchmark (BEBE), a collection of datasets with behavioral annotations, standardized modeling tasks, and evaluation metrics. BEBE is to date the largest, most taxonomically diverse, publicly available benchmark of this type, and includes 1654 hours of data collected from 149 individuals across nine taxa. We evaluate the performance of ten different machine learning methods on BEBE, and identify key challenges to be addressed in future work. Datasets, models, and evaluation code are made publicly available at https://github.com/earthspecies/BEBE, to enable community use of BEBE as a point of comparison in methods development.

Genghis Khan got his dying wish: despite attempts by archaeologists and scientists to find the Mongolian ruler's final resting place, the location remains a secret 800 years after his death. The search for his tomb, though, has inspired an innovative project that could help protect polar bears. "I randomly tuned into the radio one night and heard an expert talking about the use of synthetic aperture radar [SAR] to look for Genghis Khan's tomb," says Tom Smith, associate professor in plant and wildlife sciences at Brigham Young University (BYU) in Utah. "They were using SAR to penetrate layers of forest canopy in upper Mongolia, looking for the ruins of a burial structure." Talking to engineers, including BYU's Dr David Long, Smith learned that SAR is used by the military to detect enemy camps, tanks and vehicles hidden beneath camouflage and is being studied as a potential tool for finding avalanche survivors.

National Oceanic and Atmospheric Administration scientists are preparing to use machine learning (ML) to more easily monitor threatened ice seal populations in Alaska between April and May. Ice flows are critical to seal life cycles but are melting due to climate change -- which has hit the Arctic and sub-Arctic regions hardest. So scientists are trying to track species' population distributions. But surveying millions of aerial photographs of sea ice a year for ice seals takes months. And the data is outdated by the time statisticians analyze it and share it with the NOAA assistant regional administrator for protected resources in Juneau, according to a Microsoft blog post.

Moreland's project combines AI technology with improved cameras on a NOAA turboprop airplane that will fly over the Beaufort Sea north of Alaska this April and May, scanning and classifying the imagery to produce a population count of ice seals and polar bears that will be ready in hours instead of months. Her colleague Manuel Castellote, a NOAA affiliate scientist, will apply a similar algorithm to the recordings he'll pick up from equipment scattered across the bottom of Alaska's Cook Inlet, helping him quickly decipher how the shrinking population of endangered belugas spent its winter.

This article is by Sean Varah, founder and chief executive of MotionDSP, a company that makes advanced image processing and video analytics software. Last month the Federal Aviation Administration made a decision that marks a significant step for the commercial drone industry, permitting six movie and television production companies the right to use drones. This is the first time the FAA has allowed this type of industry exemption from the rules that prohibit drones from flying in U.S. airspace. Despite Congress' request that it develop standards in support of safe drone use by September 2015, and despite corporate America's campaigning for drone operations, the FAA has been dragging its feet. Thanks to Hollywood and the broader entertainment industry, a door has been opened for commercial drones.