چکیده مقاله
This contribution is aimed to show one of the several rising applications of theArtificial Intelligence in geophysics, i e the computation of surface wind speed over the seafrom satellite Synthetic Aperture Radar SAR images The area of interest is on the SvalbardArchipelago in the Arctic Sea In this area, the deep learning methodology based on a residualneural network ResNet , developed to retrieve wind directions from SAR at grid size ≤ 1km without external information, has been associated with a robust methodology of textureanalysis to detect the presence of sea ice over the sea, as the wind can be estimated only overthe water Avoiding to illustrate technical details concerning the ResNet and texture analysismethodologies, this contribution is focused on showing, as example, a SAR derived wind field inthe area of interest obtained using ResNet and texture analysis
کلیدواژهها
نویسندگان
Stefano Zecehetto
Faculty of Intelligent Systems Engineering and Data Science, Persian Gulf University, Bushehr, Iran
Ahmad Keshavarz
Faculty of Intelligent Systems Engineering and Data Science, Persian Gulf University, Bushehr, Iran
Parsa Shamsaddini
Faculty of Intelligent Systems Engineering and Data Science, Persian Gulf University, Bushehr, Iran
شیوه ارجاع
Zecehetto, Stefano and Keshavarz, Ahmad and Shamsaddini, Parsa,1403,Using Convolutional Neural Network in geophysics: wind field evaluation from satellite SAR images,1st International Biennial Conference of Artificial Intelligence and Data Science 2024,Bushehr
ارائهشده در
مجموعه مقالات اولین کنفرانس بین المللی دوسالانه هوش مصنوعی و علوم داده3 اردیبهشت 1403 · بوشهر