SEOULTECH researchers develop autonomous geological assessment tool
Peer-Reviewed Publication
Updates every hour. Last Updated: 1-May-2025 05:08 ET (1-May-2025 09:08 GMT/UTC)
Researchers develop a novel methodology called Roughness-CANUPO-Dip-Facet (R-C-D-F), which leverages machine learning to perform geological assessments of rock faces. R-C-D-F accurately measures dip angles and directions of rock facets by identifying important features called joint embedment points. This fully autonomous approach will help enhance precision and safety in large construction projects, such as tunnels and mines, reducing human error and improving efficiency in geological data processing.
The International Space Station (ISS) National Laboratory highlighted the rapid growth of space-based R&D in its annual report, released today by the Center for the Advancement of Science in Space® (CASIS®). Over the past fiscal year, the ISS National Lab sponsored more than 100 payloads delivered to the orbiting laboratory—the second-highest annual total to date. Also this year, ISS National Lab-related results were published in 51 peer-reviewed articles—the most ever in a year—underscoring the vital role of the ISS National Lab in advancing scientific discovery and innovation.
Weather, climate and hydrometeorology forecasts require accurate surface-atmosphere coupled modeling, but arbitrary reference heights have been used in computing surface turbulent fluxes. A team of atmospheric scientists have found optimal coupling heights for improved surface-atmosphere modeling.