576 citations · 601 across the 8 of their papers we have counts for
10 papers
Global monitoring of methane point sources using deep learning on hyperspectral radiance measurements from EMIT
Vishal V. Batchu, Michelangelo Conserva, Alex Wilson +5
Anthropogenic methane (CH4) point sources are critical drivers of near-term climate forcing, safety hazards, and system-inefficiencies. Space-based imaging spectroscopy is an emerg…
Mapping Farmed Landscapes from Remote Sensing
Michelangelo Conserva, Alex Wilson, Charlotte Stanton +2
Effective management of agricultural landscapes is critical for meeting global biodiversity targets, but efforts are hampered by the absence of detailed, large-scale ecological map…
Satellite Sunroof: High-res Digital Surface Models and Roof Segmentation for Global Solar Mapping
Vishal Batchu, Alex Wilson, Betty Peng +5
The transition to renewable energy, particularly solar, is key to mitigating climate change. Google's Solar API aids this transition by estimating solar potential from aerial image…
Cross Modal Distillation for Flood Extent Mapping
Shubhika Garg, Ben Feinstein, Shahar Timnat +4
The increasing intensity and frequency of floods is one of the many consequences of our changing climate. In this work, we explore ML techniques that improve the flood detection mo…
Multimodal contrastive learning for remote sensing tasks
Umangi Jain, Alex Wilson, Varun Gulshan
Self-supervised methods have shown tremendous success in the field of computer vision, including applications in remote sensing and medical imaging. Most popular contrastive-loss b…
A Machine Learning Data Fusion Model for Soil Moisture Retrieval
Vishal Batchu, Grey Nearing, Varun Gulshan
We develop a deep learning based convolutional-regression model that estimates the volumetric soil moisture content in the top ~5 cm of soil. Input predictors include Sentinel-1 (a…
Evaluating Self and Semi-Supervised Methods for Remote Sensing Segmentation Tasks
Chaitanya Patel, Shashank Sharma, Valerie J. Pasquarella +1
Self- and semi-supervised machine learning techniques leverage unlabeled data for improving downstream task performance. These methods are especially valuable for remote sensing ta…
Inundation Modeling in Data Scarce Regions
Zvika Ben-Haim, Vladimir Anisimov, Aaron Yonas +4
Flood forecasts are crucial for effective individual and governmental protective action. The vast majority of flood-related casualties occur in developing countries, where providin…
Grader variability and the importance of reference standards for evaluating machine learning models for diabetic retinopathy
Jonathan Krause, Varun Gulshan, Ehsan Rahimy +5
Diabetic retinopathy (DR) and diabetic macular edema are common complications of diabetes which can lead to vision loss. The grading of DR is a fairly complex process that requires…
Who Said What: Modeling Individual Labelers Improves Classification
Melody Y. Guan, Varun Gulshan, Andrew M. Dai +1
Data are often labeled by many different experts with each expert only labeling a small fraction of the data and each data point being labeled by several experts. This reduces the…