72 citations · 139 across the 7 of their papers we have counts for
9 papers
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…
Interactive Visualization and Representation Analysis Applied to Glacier Segmentation
Minxing Zheng, Xinran Miao, Kris Sankaran
Interpretability has attracted increasing attention in earth observation problems. We apply interactive visualization and representation analysis to guide interpretation of glacier…
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…
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…
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…
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…
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…
Tackling Climate Change with Machine Learning
David Rolnick, Priya L. Donti, Lynn H. Kaack +19
Climate change is one of the greatest challenges facing humanity, and we, as machine learning experts, may wonder how we can help. Here we describe how machine learning can be a po…
Visualizing the Consequences of Climate Change Using Cycle-Consistent Adversarial Networks
Victor Schmidt, Alexandra Luccioni, S. Karthik Mukkavilli +4
We present a project that aims to generate images that depict accurate, vivid, and personalized outcomes of climate change using Cycle-Consistent Adversarial Networks (CycleGANs).…