<script type="text/javascript">
<!--
document.write('<div id="oa_widget"></div>');
document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=undefined&type=result"></script>');
-->
</script>

COPY SCRIPT

For further information contact us at helpdesk@openaire.eu

Detecting floating objects using deep learning and Sentinel-2 imagery (Jupyter Notebook) published in the Environmental Data Science book

Name: Detecting floating objects using deep learning and Sentinel-2 imagery (Jupyter Notebook) published in the Environmental Data Science book
Keywords: Deep learning, FOS: Earth and related environmental sciences, Oceanography, Environmental Data Science, Modelling

integration_instructionsResearch softwarekeyboard_double_arrow_right Software 02 Sep 2023Publisher:Zenodo

Authors: Mifdal, Jamila; Carmo, Raquel; This EDS book notebook contributors;

doi: 10.5281/zenodo.8308843 , 10.5281/zenodo.8310968 , 10.5281/zenodo.10798332 , 10.5281/zenodo.10808251 , 10.5281/zenodo.10808173

Detecting floating objects using deep learning and Sentinel-2 imagery (Jupyter Notebook) published in the Environmental Data Science book

- Summary
- Subjects
- Related research
  (1)
- Metrics

Abstract

Notebook developed to demonstrate the use of deep neural networks for the detection of floating objects on Sentinel-2 data.

If you use this software, please cite it using the metadata from this file.

Related Organizations

European Space Agency
France

Keywords

Deep learning, FOS: Earth and related environmental sciences, Oceanography, Environmental Data Science, Modelling

1 Research products, page 1 of 1

Detecting floating objects using deep learning and Sentinel-2 imagery
2023HasVersion

Impact byBIP!

	citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	0
	popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.	Average
	influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	Average
	impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.	Average

Usage byUsageCounts

visibility

views

2
views
Powered by

Found an issue? Give us feedback

visibility

Average