39 citations · 102 across the 14 of their papers we have counts for
14 papers
AI Security Priorities: A Field-Wide Agenda
Gil Gekker, Rachel Steratore, Everett Smith +9
As AI systems are rapidly integrated into critical economic, governmental, and national security functions, the gap between AI adoption and AI security readiness continues to widen…
High-Resolution Building and Road Detection from Sentinel-2
Wojciech Sirko, Emmanuel Asiedu Brempong, Juliana T. C. Marcos +9
Mapping buildings and roads automatically with remote sensing typically requires high-resolution imagery, which is expensive to obtain and often sparsely available. In this work we…
AI Increases Global Access to Reliable Flood Forecasts
Grey Nearing, Deborah Cohen, Vusumuzi Dube +15
Floods are one of the most common natural disasters, with a disproportionate impact in developing countries that often lack dense streamflow gauge networks. Accurate and timely war…
Accelerating Physics Simulations with TPUs: An Inundation Modeling Example
Damien Pierce, R. Lily Hu, Yusef Shafi +4
Recent advancements in hardware accelerators such as Tensor Processing Units (TPUs) speed up computation time relative to Central Processing Units (CPUs) not only for machine learn…
Flood forecasting with machine learning models in an operational framework
Sella Nevo, Efrat Morin, Adi Gerzi Rosenthal +28
The operational flood forecasting system by Google was developed to provide accurate real-time flood warnings to agencies and the public, with a focus on riverine floods in large,…
On pseudo-absence generation and machine learning for locust breeding ground prediction in Africa
Ibrahim Salihu Yusuf, Kale-ab Tessera, Thomas Tumiel +4
Desert locust outbreaks threaten the food security of a large part of Africa and have affected the livelihoods of millions of people over the years. Machine learning (ML) has been…
Physics-Aware Downsampling with Deep Learning for Scalable Flood Modeling
Niv Giladi, Zvika Ben-Haim, Sella Nevo +2
Background: Floods are the most common natural disaster in the world, affecting the lives of hundreds of millions. Flood forecasting is therefore a vitally important endeavor, typi…
ML-based Flood Forecasting: Advances in Scale, Accuracy and Reach
Sella Nevo, Gal Elidan, Avinatan Hassidim +4
Floods are among the most common and deadly natural disasters in the world, and flood warning systems have been shown to be effective in reducing harm. Yet the majority of the worl…
HydroNets: Leveraging River Structure for Hydrologic Modeling
Zach Moshe, Asher Metzger, Gal Elidan +3
Accurate and scalable hydrologic models are essential building blocks of several important applications, from water resource management to timely flood warnings. However, as the cl…
Accurate Hydrologic Modeling Using Less Information
Guy Shalev, Ran El-Yaniv, Daniel Klotz +3
Joint models are a common and important tool in the intersection of machine learning and the physical sciences, particularly in contexts where real-world measurements are scarce. R…
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…
Spectral Algorithm for Low-rank Multitask Regression
Yotam Gigi, Ami Wiesel, Sella Nevo +3
Multitask learning, i.e. taking advantage of the relatedness of individual tasks in order to improve performance on all of them, is a core challenge in the field of machine learnin…
ML for Flood Forecasting at Scale
Sella Nevo, Vova Anisimov, Gal Elidan +11
Effective riverine flood forecasting at scale is hindered by a multitude of factors, most notably the need to rely on human calibration in current methodology, the limited amount o…
Towards Global Remote Discharge Estimation: Using the Few to Estimate The Many
Yotam Gigi, Gal Elidan, Avinatan Hassidim +5
Learning hydrologic models for accurate riverine flood prediction at scale is a challenge of great importance. One of the key difficulties is the need to rely on in-situ river disc…