A survey of machine learning and deep learning in remote sensing of geological environment: Challenges, advances, and opportunities

W Han, X Zhang, Y Wang, L Wang, X Huang… - ISPRS Journal of …, 2023 - Elsevier
Due to limited resources and environmental pollution, monitoring the geological
environment has become essential for many countries' sustainable development. As various …

A review of deep-learning methods for change detection in multispectral remote sensing images

EJ Parelius - Remote Sensing, 2023 - mdpi.com
Remote sensing is a tool of interest for a large variety of applications. It is becoming
increasingly more useful with the growing amount of available remote sensing data …

Episodic dynamic change linked to damage on the Thwaites Glacier Ice Tongue

T Surawy-Stepney, AE Hogg, SL Cornford… - Nature …, 2023 - nature.com
The stability and dynamics of Thwaites Glacier depend on the structural properties of its
marine terminus; however, the relationship between these variables on the floating ice …

A data-driven deep learning model for weekly sea ice concentration prediction of the pan-arctic during the melting season

Y Ren, X Li, W Zhang - IEEE Transactions on Geoscience and …, 2022 - ieeexplore.ieee.org
This study proposes a purely data-driven model for the weekly prediction of daily sea ice
concentration (SIC) of the pan-Arctic (90 N, 45 N, 180 E, 180 W) during the melting season …

AutoTerm: an automated pipeline for glacier terminus extraction using machine learning and a “big data” repository of Greenland glacier termini

E Zhang, G Catania, DT Trugman - The Cryosphere, 2023 - tc.copernicus.org
Ice sheet marine margins via outlet glaciers are susceptible to climate change and are
expected to respond through retreat, steepening, and acceleration, although with significant …