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Found 17 papers, 4 papers with code
Physics-Informed Machine Learning On Polar Ice: A Survey
The mass loss of the polar ice sheets contributes considerably to ongoing sea-level rise and changing ocean circulation, leading to coastal flooding and risking the homes and livelihoods of tens of millions of people globally.
TinyVQA: Compact Multimodal Deep Neural Network for Visual Question Answering on Resource-Constrained Devices
Traditional machine learning models often require powerful hardware, making them unsuitable for deployment on resource-limited devices.
Graph Neural Networks as Fast and High-fidelity Emulators for Finite-Element Ice Sheet Modeling
Although the finite element approach of the Ice-sheet and Sea-level System Model (ISSM) solves ice dynamics problems governed by Stokes equations quickly and accurately, such numerical modeling requires intensive computation on central processing units (CPU).
Multi-task Deep Convolutional Network to Predict Sea Ice Concentration and Drift in the Arctic Ocean
The weight values of the WAMs imply that SIC information plays a more critical role in SID prediction, compared to that of SID information in SIC prediction, and information sharing is more active in sea ice edges (seasonal sea ice) than in the central Arctic (multi-year sea ice).
Skip-WaveNet: A Wavelet based Multi-scale Architecture to Trace Firn Layers in Radar Echograms
Echograms created from airborne radar sensors capture the profile of firn layers present on top of an ice sheet.
Prediction of Annual Snow Accumulation Using a Recurrent Graph Convolutional Approach
The precise tracking and prediction of polar ice layers can unveil historic trends in snow accumulation.
Prediction of Deep Ice Layer Thickness Using Adaptive Recurrent Graph Neural Networks
As we deal with the effects of climate change and the increase of global atmospheric temperatures, the accurate tracking and prediction of ice layers within polar ice sheets grows in importance.
Polar-VQA: Visual Question Answering on Remote Sensed Ice sheet Imagery from Polar Region
With the advancement of deep learning techniques, we can now extract high-level information from the ice sheet data (e. g., estimating the ice layer thickness, predicting the ice accumulation for upcoming years, etc.).
Recurrent Graph Convolutional Networks for Spatiotemporal Prediction of Snow Accumulation Using Airborne Radar
The accurate prediction and estimation of annual snow accumulation has grown in importance as we deal with the effects of climate change and the increase of global atmospheric temperatures.
Efficient Semantic Segmentation on Edge Devices
Semantic segmentation works on the computer vision algorithm for assigning each pixel of an image into a class.
RescueNet: A High Resolution UAV Semantic Segmentation Benchmark Dataset for Natural Disaster Damage Assessment
Recent advancements in computer vision and deep learning techniques have facilitated notable progress in scene understanding, thereby assisting rescue teams in achieving precise damage assessment.
VQA-Aid: Visual Question Answering for Post-Disaster Damage Assessment and Analysis
Visual Question Answering system integrated with Unmanned Aerial Vehicle (UAV) has a lot of potentials to advance the post-disaster damage assessment purpose.
Attention Based Semantic Segmentation on UAV Dataset for Natural Disaster Damage Assessment
The detrimental impacts of climate change include stronger and more destructive hurricanes happening all over the world.
Regression Networks For Calculating Englacial Layer Thickness
Ice thickness estimation is an important aspect of ice sheet studies.
FloodNet: A High Resolution Aerial Imagery Dataset for Post Flood Scene Understanding
This dataset demonstrates the post flooded damages of the affected areas.
Comprehensive Semantic Segmentation on High Resolution UAV Imagery for Natural Disaster Damage Assessment
In this paper, we present a large-scale hurricane Michael dataset for visual perception in disaster scenarios, and analyze state-of-the-art deep neural network models for semantic segmentation.
Deep Ice Layer Tracking and Thickness Estimation using Fully Convolutional Networks
Global warming is rapidly reducing glaciers and ice sheets across the world.
