HED-UNet: Combined segmentation and edge detection for monitoring the Antarctic coastline
Deep learning-based coastline detection algorithms have begun to outshine traditional
statistical methods in recent years. However, they are usually trained only as single-purpose …
statistical methods in recent years. However, they are usually trained only as single-purpose …
[LIVRE][B] Deep learning for the Earth Sciences: A comprehensive approach to remote sensing, climate science and geosciences
DEEP LEARNING FOR THE EARTH SCIENCES Explore this insightful treatment of deep
learning in the field of earth sciences, from four leading voices Deep learning is a …
learning in the field of earth sciences, from four leading voices Deep learning is a …
Automated glacier extraction using a Transformer based deep learning approach from multi-sensor remote sensing imagery
Glaciers serve as sensitive indicators of climate change, making accurate glacier boundary
delineation crucial for understanding their response to environmental and local factors …
delineation crucial for understanding their response to environmental and local factors …
An automated, generalized, deep-learning-based method for delineating the calving fronts of Greenland glaciers from multi-sensor remote sensing imagery
In the past two decades, the data volume of remote sensing imagery in the polar regions has
increased dramatically. The calving fronts of many Greenland glaciers have been …
increased dramatically. The calving fronts of many Greenland glaciers have been …
Episodic dynamic change linked to damage on the Thwaites Glacier Ice Tongue
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 …
marine terminus; however, the relationship between these variables on the floating ice …
Automated extraction of antarctic glacier and ice shelf fronts from sentinel-1 imagery using deep learning
Sea level rise contribution from the Antarctic ice sheet is influenced by changes in glacier
and ice shelf front position. Still, little is known about seasonal glacier and ice shelf front …
and ice shelf front position. Still, little is known about seasonal glacier and ice shelf front …
Prediction of monthly Arctic sea ice concentrations using satellite and reanalysis data based on convolutional neural networks
Changes in Arctic sea ice affect atmospheric circulation, ocean current, and polar
ecosystems. There have been unprecedented decreases in the amount of Arctic sea ice due …
ecosystems. There have been unprecedented decreases in the amount of Arctic sea ice due …
A novel method for automated supraglacial lake map** in Antarctica using Sentinel-1 SAR imagery and deep learning
Supraglacial meltwater accumulation on ice sheets can be a main driver for accelerated ice
discharge, mass loss, and global sea-level-rise. With further increasing surface air …
discharge, mass loss, and global sea-level-rise. With further increasing surface air …
Deep learning speeds up ice flow modelling by several orders of magnitude
This paper introduces the Instructed Glacier Model (IGM)–a model that simulates ice
dynamics, mass balance and its coupling to predict the evolution of glaciers, icefields or ice …
dynamics, mass balance and its coupling to predict the evolution of glaciers, icefields or ice …
AutoTerm: an automated pipeline for glacier terminus extraction using machine learning and a “big data” repository of Greenland glacier termini
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 …
expected to respond through retreat, steepening, and acceleration, although with significant …