Michael Ramsey

Grab a front-row seat with Michael Ramsay, fitness industry trailblazer, as he talks about turning challenges into triumphs and setting his fitness franchises apart in a crowded market.

Harley Reid made a massive statement as the Eagles lifted themselves off the bottom of the ladder.

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The Harley Reid hype train continues to gather steam after the No.1 draft pick unleashed a stunning performance in just his fifth game to help lead West Coast to a drought-breaking AFL victory.

Most ORV enthusiasts and trail users carefully plan their outings. They get wet, dirty and allow for some downtime — all parts of summer fun in Northern Michigan — but

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Our weekly program, Making Democracy Work, is a collaboration between KSMU Radio and the League of Women Voters of Southwest Missouri. It's hosted and produced by volunteers from the LWV, and it airs each Monday morning at 9:45 on KSMU.

The Michigan Department of Agriculture and Rural Development (MDARD) confirmed a second population of spotted lanternfly in Michigan. This new pest, only confirmed in Oakland County up to this year,

A young woman has appeared at Letterkenny District Court on a total of 84 theft and fraud charges.

Twenty-one years of Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) global thermal infrared (TIR) acquisitions provide a large amount of data for volcano monitoring. These data, with high spatial and spectral resolution, enable routine investigations of volcanoes in remote and inaccessible regions, including those with no ground-based monitoring. However, the dataset is too large to be manually analyzed on a global basis. Here, we systematically process the data over several volcanoes using a deep learning algorithm to automatically extract volcanic thermal anomalies. We explore the application of a convolutional neural network (CNN), specifically UNET, to detect subtle to intense anomalies exploiting the spatial relationships of the volcanic features. We employ a supervised UNET network trained with the largest (1500) labeled dataset of ASTER TIR images from five different volcanoes, namely, Etna (Italy), Popocatépetl (Mexico), Lascar (Chile), Fuego (Guatemala), and Kliuchevskoi (Russia). We show that our approach achieves high accuracy (93%) with excellent generalization capabilities. The effectiveness of our model for detecting the full range of thermal emission is shown for volcanoes with very different styles of activity and tested at Vulcano (Italy). The results demonstrate the potential applicability of the proposed approach to the development of automated thermal analysis systems at the global scale using future TIR data such as the planned NASA Surface Biology and Geology (SBG) mission.