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multi-mode-cnn-pytorch

A PyTorch implementation of the Multi-Mode CNN to reconstruct Chlorophyll-a time series in the global ocean from oceanic and atmospheric physical drivers

https://github.com/joanar/multi-mode-cnn-pytorch

Science Score: 67.0%

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    Low similarity (11.5%) to scientific vocabulary

Keywords

attention-mechanisms biogeochemical-regions chlorophyll-a climate convolutional-neural-networks deep-learning earth-observation global-scale interpretability marine-science ocean-color oceanography physical-drivers phytoplankton pytorch regression remote-sensing satellite-data seasonal-interannual-timescale time-series
Last synced: 6 months ago · JSON representation ·

Repository

A PyTorch implementation of the Multi-Mode CNN to reconstruct Chlorophyll-a time series in the global ocean from oceanic and atmospheric physical drivers

Basic Info
  • Host: GitHub
  • Owner: JoanaR
  • License: mit
  • Language: Jupyter Notebook
  • Default Branch: main
  • Homepage:
  • Size: 8.97 MB
Statistics
  • Stars: 8
  • Watchers: 1
  • Forks: 2
  • Open Issues: 1
  • Releases: 1
Topics
attention-mechanisms biogeochemical-regions chlorophyll-a climate convolutional-neural-networks deep-learning earth-observation global-scale interpretability marine-science ocean-color oceanography physical-drivers phytoplankton pytorch regression remote-sensing satellite-data seasonal-interannual-timescale time-series
Created over 3 years ago · Last pushed almost 3 years ago
Metadata Files
Readme License Citation

README.md

A Multi-Mode Convolutional Neural Network (CNNMM) to reconstruct satellite-derived Chlorophyll-a time series in the global ocean from physical drivers

This repository contains the code of the model presented in the paper A Multi-Mode Convolutional Neural Network to reconstruct Chlorophyll-a time series in the global ocean from physical drivers.

Contents

This repository contains the following PyTorch code: - Implementation of the multi-mode CNNMM8 Chl time series regression from oceanic and atmospheric predictors :

Results

Our model achieves the following performance on INDIGO Benchmark dataset :

| Model name | r2 | RMSE | Slope|Seas|Inter|N param|Time computation|Km travelled by car| | ---|--- | --- |--- | -- |-- |-- | -- |-- | | CNNMM8|0.87 | 0.28 |0.90 | 1.00 |0.96 |803 920 | 39 h |8.9 |

See the paper for more details.

Requirements

Python Libraries :

  • torch==1.4.0
  • torchvision==0.5.0
  • numpy==1.18.1
  • carbontracker==1.1.5

INDIGO dataset download :

The benchmark formated dataset is available for download here. You can also find the required files to run the code in this repository. This dataset was built from the following source of data :

| Proxy used as predictors | Acronyme | Products | Initial spatio-temporal resolutions| | ------------------ |--- | --- |--- | | Sea Surface Temperature | SST | Reyn_SmithOIv2 SST dataset |Monthly on a 1◦ × 1◦ spatial grid| | Sea Level Anomaly |SLA |Ssalto/Duacs merged product of CNES/SALP project |Weekly on a 1/3◦ × 1/3◦ spatial grid| | Zonal and Meridional surface winds | Uera, Vera |Atmospheric model reanalysis ERA interim 4 |Every 5-days on a 0.25◦ × 0.25◦ spatial grid| | Zonal and Meridional surface total currents | u,v |OSCAR unfiltered satellite product |Every 5-days on a 0.25◦ × 0.25◦ spatial grid| | Short-wave radiations | SW |NCEP/NCAR Numerical reanalysis |Daily on a 2◦ grid| | Binary continental mask | mask ||| | Bathymetry | bathy | GEBCO|15 arc seconds|

Animation

Chl recontructed data over [2012-2015] :

https://github.com/JoanaR/multi-mode-CNN-pytorch/assets/12017107/c0c16715-7a25-4a3a-a810-672ce3f92f2d

Chl reference satellite data over [2012-2015] :

https://github.com/JoanaR/multi-mode-CNN-pytorch/assets/12017107/44148340-d8dc-4b92-b075-e7ea0e3ec0cd

References

Roussillon Joana, Fablet Ronan, Gorgues Thomas, Drumetz Lucas, Littaye Jean, Martinez Elodie (2023). A Multi-Mode Convolutional Neural Network to reconstruct satellite-derived Chlorophyll-a time series in the global ocean from physical drivers. Frontiers in marine science. doi: 10.3389/fmars.2023.1077623

Roussillon Joana, Fablet Ronan, Gorgues Thomas, Drumetz Lucas, Littaye Jean, Martinez Elodie (2022). satellIte phytoplaNkton Drivers In the Global Ocean over 1998-2015 (INDIGO Benchmark dataset). SEANOE. https://doi.org/10.17882/91910

DOI

Owner

  • Login: JoanaR
  • Kind: user
  • Location: Brest
  • Company: LOPS

Citation (CITATION.cff) 

multi-mode-CNN-version: 1.1.0
message: "If you use this software, please cite it as below."
authors:
  - family-names: Roussillon
    given-names: Joana
title: multi-mode-CNN: v1.1.0
version: v1.1.0
date-released: 2023-05-17

GitHub Events

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