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Found 7 papers, 4 papers with code
Comparison of Cross-Entropy, Dice, and Focal Loss for Sea Ice Type Segmentation
Up-to-date sea ice charts are crucial for safer navigation in ice-infested waters.
Deep Learning on SAR Imagery: Transfer Learning Versus Randomly Initialized Weights
Deploying deep learning on Synthetic Aperture Radar (SAR) data is becoming more common for mapping purposes.
Enhancing sea ice segmentation in Sentinel-1 images with atrous convolutions
Multiclass ice type classification is more challenging, and even though our models achieve 2% improvement in weighted F1 average compared to the baseline U-Net, test weighted F1 is generally between 0. 6 and 0. 80.
A spatiotemporal machine learning approach to forecasting COVID-19 incidence at the county level in the USA
We highlight that the underutilization of data-driven forecasting of disease spread prior to COVID-19 is likely due to the lack of sufficient data available for previous diseases, in addition to the recent advances in machine learning methods for spatiotemporal forecasting.
Geovisual Analytics and Interactive Machine Learning for Situational Awareness
The first responder community has traditionally relied on calls from the public, officially-provided geographic information and maps for coordinating actions on the ground.
City-level Geolocation of Tweets for Real-time Visual Analytics
In this paper, we adapt, improve, and evaluate a state-of-the-art deep learning model for city-level geolocation prediction, and integrate it with a visual analytics system tailored for real-time situational awareness.
Interactive Learning for Identifying Relevant Tweets to Support Real-time Situational Awareness
We present a novel interactive learning framework to improve the classification process in which the user iteratively corrects the relevancy of tweets in real-time to train the classification model on-the-fly for immediate predictive improvements.
