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b34facfa-cea8-48f5-89f6-f11ce00812a9

Showcasing a deep learning model for detecting floating objects

https://github.com/eds-book/b34facfa-cea8-48f5-89f6-f11ce00812a9

Science Score: 67.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • ✓
    CITATION.cff file
    Found CITATION.cff file
  • ✓
    codemeta.json file
    Found codemeta.json file
  • ✓
    .zenodo.json file
    Found .zenodo.json file
  • ✓
    DOI references
    Found 2 DOI reference(s) in README
  • ✓
    Academic publication links
    Links to: zenodo.org
  • ○
    Committers with academic emails
  • ○
    Institutional organization owner
  • ○
    JOSS paper metadata
  • ○
    Scientific vocabulary similarity
    Low similarity (11.2%) to scientific vocabulary

Keywords

deep-learning environmental-data-science modelling oceanography
Last synced: 6 months ago · JSON representation ·

Repository

Showcasing a deep learning model for detecting floating objects

Basic Info
  • Host: GitHub
  • Owner: eds-book
  • License: other
  • Language: Jupyter Notebook
  • Default Branch: main
  • Homepage:
  • Size: 8.35 MB
Statistics
  • Stars: 0
  • Watchers: 2
  • Forks: 0
  • Open Issues: 1
  • Releases: 7
Topics
deep-learning environmental-data-science modelling oceanography
Created almost 4 years ago · Last pushed 8 months ago
Metadata Files
Readme License Citation

README.md

Detecting floating objects using deep learning and Sentinel-2 imagery

Continuous integration badge Binder doi notebook review

thumbnail

How to run

Running on Binder

The notebook is designed to be launched from Binder.

Click the Launch Binder button at the top level of the repository

Running locally

You may also download the notebook from GitHub to run it locally: 1. Open your terminal

  1. Check your conda install with conda --version. If you don't have conda, install it by following these instructions (see here)

  2. Clone the repository bash git clone https://github.com/eds-book-gallery/b34facfa-cea8-48f5-89f6-f11ce00812a9.git

  3. Move into the cloned repository bash cd b34facfa-cea8-48f5-89f6-f11ce00812a9

  4. Create and activate your environment from the .binder/environment.yml file bash conda env create -f .binder/environment.yml conda activate b34facfa-cea8-48f5-89f6-f11ce00812a9

  5. Launch the jupyter interface of your preference, notebook, jupyter notebook or lab jupyter lab

Owner

  • Name: Environmental Data Science Book
  • Login: eds-book
  • Kind: organization
  • Email: environmental.ds.book@gmail.com

Organisation repo of EDS book for governance, outreach and other community-led activities

Citation (CITATION.cff) 

cff-version: 1.2.0
message: Please cite the following works when using this project.
abstract: >-
  Notebook developed to demonstrate the use of deep neural networks for the
  detection of floating objects on Sentinel-2 data.
title: >-
  Detecting floating objects using deep learning and Sentinel-2 imagery (Jupyter
  Notebook) published in the Environmental Data Science book
authors:
  - family-names: Mifdal
    given-names: Jamila
    affiliation: ESA Phi-Lab
    orcid: 0000-0002-9480-7387
  - family-names: Carmo
    given-names: Raquel
    affiliation: ESA Phi-Lab
    orcid: 0000-0002-9480-7387
    email: raquelarscarmo@gmail.com
date-released: '2023-09-01'
contact:
  - family-names: Carmo
    given-names: Raquel
    affiliation: ESA Phi-Lab
    orcid: 0000-0002-9480-7387
    email: raquelarscarmo@gmail.com
identifiers:
  - description: Open review report for this notebook
    type: url
    value: https://github.com/alan-turing-institute/environmental-ds-book/pull/22
keywords:
  - Oceans
  - Modelling
  - Standard
  - Python
license: MIT
license-url: https://opensource.org/license/MIT
repository: https://github.com/eds-book/b34facfa-cea8-48f5-89f6-f11ce00812a9
type: software
version: v2025.6.0

GitHub Events

Total
  • Release event: 1
  • Issue comment event: 1
  • Push event: 48
  • Pull request event: 1
  • Create event: 3
Last Year
  • Release event: 1
  • Issue comment event: 1
  • Push event: 48
  • Pull request event: 1
  • Create event: 3

Committers

Last synced: 7 months ago

All Time
  • Total Commits: 131
  • Total Committers: 1
  • Avg Commits per committer: 131.0
  • Development Distribution Score (DDS): 0.0
Past Year
  • Commits: 59
  • Committers: 1
  • Avg Commits per committer: 59.0
  • Development Distribution Score (DDS): 0.0
Top Committers
Name Email Commits
Alejandro © a****c@g****m 131