most citedNetherlands Dataset: A New Public Dataset for Machine Learning in Seismic Interpretation

39 citations · 84 across the 5 of their papers we have counts for

collaborators

5 papers

cs.LG20216 cited

Extreme Precipitation Seasonal Forecast Using a Transformer Neural Network

Daniel Salles Civitarese, Daniela Szwarcman, Bianca Zadrozny +1

An impact of climate change is the increase in frequency and intensity of extreme precipitation events. However, confidently predicting the likelihood of extreme precipitation at s…

cs.LG20217 cited

A modular framework for extreme weather generation

Bianca Zadrozny, Campbell D. Watson, Daniela Szwarcman +4

Extreme weather events have an enormous impact on society and are expected to become more frequent and severe with climate change. In this context, resilience planning becomes cruc…

eess.IV201922 cited

Semantic Segmentation of Seismic Images

Daniel Civitarese, Daniela Szwarcman, Emilio Vital Brazil +1

Almost all work to understand Earth's subsurface on a large scale relies on the interpretation of seismic surveys by experts who segment the survey (usually a cube) into layers; a…

cs.LG201939 cited

Netherlands Dataset: A New Public Dataset for Machine Learning in Seismic Interpretation

Reinaldo Mozart Silva, Lais Baroni, Rodrigo S. Ferreira +3

Machine learning and, more specifically, deep learning algorithms have seen remarkable growth in their popularity and usefulness in the last years. This is arguably due to three ma…

physics.geo-ph201910 cited

Penobscot Dataset: Fostering Machine Learning Development for Seismic Interpretation

Lais Baroni, Reinaldo Mozart Silva, Rodrigo S. Ferreira +3

We have seen in the past years the flourishing of machine and deep learning algorithms in several applications such as image classification and segmentation, object detection and r…