activity
20192021
most citedHighRes-net: Recursive Fusion for Multi-Frame Super-Resolution of Satellite Imagery

72 citations · 137 across the 5 of their papers we have counts for

collaborators

8 papers

cs.CV20222 cited

Sampling Strategy for Fine-Tuning Segmentation Models to Crisis Area under Scarcity of Data

Adrianna Janik, Kris Sankaran

The use of remote sensing in humanitarian crisis response missions is well-established and has proven relevant repeatedly. One of the problems is obtaining gold annotations as it i…

cs.CV202137 cited

Interpretability of a Deep Learning Model in the Application of Cardiac MRI Segmentation with an ACDC Challenge Dataset

Adrianna Janik, Jonathan Dodd, Georgiana Ifrim +2

Cardiac Magnetic Resonance (CMR) is the most effective tool for the assessment and diagnosis of a heart condition, which malfunction is the world's leading cause of death. Software…

cs.CV202011 cited

Machine Learning for Glacier Monitoring in the Hindu Kush Himalaya

Shimaa Baraka, Benjamin Akera, Bibek Aryal +7

Glacier mapping is key to ecological monitoring in the hkh region. Climate change poses a risk to individuals whose livelihoods depend on the health of glacier ecosystems. In this…

cs.CV202072 cited

HighRes-net: Recursive Fusion for Multi-Frame Super-Resolution of Satellite Imagery

Michel Deudon, Alfredo Kalaitzis, Israel Goytom +7

Generative deep learning has sparked a new wave of Super-Resolution (SR) algorithms that enhance single images with impressive aesthetic results, albeit with imaginary details. Mul…

physics.ao-ph2020

Modeling Cloud Reflectance Fields using Conditional Generative Adversarial Networks

Victor Schmidt, Mustafa Alghali, Kris Sankaran +2

We introduce a conditional Generative Adversarial Network (cGAN) approach to generate cloud reflectance fields (CRFs) conditioned on large scale meteorological variables such as se…

eess.IV20192 cited

Applying Knowledge Transfer for Water Body Segmentation in Peru

Jessenia Gonzalez, Debjani Bhowmick, Cesar Beltran +2

In this work, we present the application of convolutional neural networks for segmenting water bodies in satellite images. We first use a variant of the U-Net model to segment rive…