no code implementations • 20 Dec 2023 • Takuya Kurihana, Kyongmin Yeo, Daniela Szwarcman, Bruce Elmegreen, Karthik Mukkavilli, Johannes Schmude, Levente Klein
To mitigate global warming, greenhouse gas sources need to be resolved at a high spatial resolution and monitored in time to ensure the reduction and ultimately elimination of the pollution source.
1 code implementation • 28 Oct 2023 • Johannes Jakubik, Sujit Roy, C. E. Phillips, Paolo Fraccaro, Denys Godwin, Bianca Zadrozny, Daniela Szwarcman, Carlos Gomes, Gabby Nyirjesy, Blair Edwards, Daiki Kimura, Naomi Simumba, Linsong Chu, S. Karthik Mukkavilli, Devyani Lambhate, Kamal Das, Ranjini Bangalore, Dario Oliveira, Michal Muszynski, Kumar Ankur, Muthukumaran Ramasubramanian, Iksha Gurung, Sam Khallaghi, Hanxi, Li, Michael Cecil, Maryam Ahmadi, Fatemeh Kordi, Hamed Alemohammad, Manil Maskey, Raghu Ganti, Kommy Weldemariam, Rahul Ramachandran
This paper introduces a first-of-a-kind framework for the efficient pre-training and fine-tuning of foundational models on extensive geospatial data.
no code implementations • 23 May 2023 • Qidong Yang, Alex Hernandez-Garcia, Paula Harder, Venkatesh Ramesh, Prasanna Sattegeri, Daniela Szwarcman, Campbell D. Watson, David Rolnick
In this work, we propose a downscaling method based on the Fourier neural operator.
1 code implementation • 8 Aug 2022 • Paula Harder, Alex Hernandez-Garcia, Venkatesh Ramesh, Qidong Yang, Prasanna Sattigeri, Daniela Szwarcman, Campbell Watson, David Rolnick
In order to conserve physical quantities, here we introduce methods that guarantee statistical constraints are satisfied by a deep learning downscaling model, while also improving their performance according to traditional metrics.
no code implementations • 14 Jul 2021 • Daniel Salles Civitarese, Daniela Szwarcman, Bianca Zadrozny, Campbell Watson
An impact of climate change is the increase in frequency and intensity of extreme precipitation events.
no code implementations • 5 Feb 2021 • Bianca Zadrozny, Campbell D. Watson, Daniela Szwarcman, Daniel Civitarese, Dario Oliveira, Eduardo Rodrigues, Jorge Guevara
Extreme weather events have an enormous impact on society and are expected to become more frequent and severe with climate change.
no code implementations • 10 May 2019 • Daniel Civitarese, Daniela Szwarcman, Emilio Vital Brazil, Bianca Zadrozny
We compare our approach with two well-known deep neural network topologies: Fully Convolutional Network and U-Net.
no code implementations • 26 Mar 2019 • Reinaldo Mozart Silva, Lais Baroni, Rodrigo S. Ferreira, Daniel Civitarese, Daniela Szwarcman, Emilio Vital Brazil
In this work, we present the Netherlands interpretation dataset, a contribution to the development of machine learning in seismic interpretation.