DINCAE.jl

DINCAE (Data-Interpolating Convolutional Auto-Encoder) is a neural network to reconstruct missing data in satellite observations. This repository contains the Julia port of DINCAE. The original Python code is no longer maintained.

Utilities (for plotting and data preparation) are available in a separate repository https://github.com/gher-uliege/DINCAE_utils.jl

The method is described in the following articles:

• Barth, A., Alvera-Azcárate, A., Licer, M., & Beckers, J.-M. (2020). DINCAE 1.0: a convolutional neural network with error estimates to reconstruct sea surface temperature satellite observations. Geoscientific Model Development, 13(3), 1609–1622. https://doi.org/10.5194/gmd-13-1609-2020
• Barth, A., Alvera-Azcárate, A., Troupin, C., & Beckers, J.-M. (2022). DINCAE 2.0: multivariate convolutional neural network with error estimates to reconstruct sea surface temperature satellite and altimetry observations. Geoscientific Model Development, 15(5), 2183–2196. https://doi.org/10.5194/gmd-15-2183-2022

(click here for the BibTeX entry).

Panel (a) is the original data where we have added clouds (panel (b)). The reconstuction based on the data in panel (b) is shown in panel (c) together with its expected standard deviation error (panel (d))

DINCAE is intended to be used with a GPU with CUDA support (NVIDIA GPU) or with ROCm support (AMD GPU). The code can also run on a CPU but which will be quite slow.

Installation

You need Julia (version 1.9 or later) to run DINCAE. The command line interface of Julia is sufficient for DINCAE. If you are using Linux (on a x86_64 CPU), installing and running Julia is as easy as running these shell commands:

curl -fsSL https://install.julialang.org | sh

For more information, other platforms and the current julia version, please see platform specific instructions for further installation instructions. You can check the latest available version of Julia at https://julialang.org/downloads but avoid beta releases and release candidates if you are new to Julia.

Inside a Julia terminal, you can download and install DINCAE and DINCAE_utils by issuing these commands:

using Pkg
Pkg.add(url="https://github.com/gher-uliege/DINCAE.jl", rev="main")
Pkg.add(url="https://github.com/gher-uliege/DINCAE_utils.jl", rev="main")

CUDA/AMDGPU support

To enable (optional) CUDA support for NVIDIA GPUs one need to install also the packages CUDA and cuDNN:

using Pkg
Pkg.add(["CUDA","cuDNN"])

To confirm that CUDA is functional, use the following command:

using CUDA
CUDA.functional()

This command should return true.

For AMD GPUs, install the package AMDGPU:

using Pkg
Pkg.add("AMDGPU")

To confirm that the AMD GPU is functional, use:

using AMDGPU
AMDGPU.functional()

With some adaptions to DINCAE.jl, one can probably use GPUs on macOS using the Metal programming framework. PRs to implement support of this GPU would be very welcome.

After this, you should be able to load DINCAE with:

using DINCAE

Updating DINCAE

To update DINCAE, run the following command and restart Julia (or restart the jupyter notebook kernel using Kernel -> Restart):

using Pkg
Pkg.update("DINCAE")

Note that Julia does not directly delete the previous installed version. To check if you have the latest version run the following command:

using Pkg
Pkg.status()

The latest version number is available from here.

Documentation

More information is available in the documentation and the tutorial (available as script and jupyter notebook).

Publications

About the code

• Barth, A., Alvera-Azcárate, A., Licer, M., & Beckers, J.-M. (2020). DINCAE 1.0: a convolutional neural network with error estimates to reconstruct sea surface temperature satellite observations. Geoscientific Model Development, 13(3), 1609–1622. https://doi.org/10.5194/gmd-13-1609-2020
• Barth, A., Alvera-Azcárate, A., Troupin, C., & Beckers, J.-M. (2022). DINCAE 2.0: multivariate convolutional neural network with error estimates to reconstruct sea surface temperature satellite and altimetry observations. Geoscientific Model Development, 15(5), 2183–2196. https://doi.org/10.5194/gmd-15-2183-2022

Applications

• Han, Z., He, Y., Liu, G., & Perrie, W. (2020). Application of DINCAE to Reconstruct the Gaps in Chlorophyll-a Satellite Observations in the South China Sea and West Philippine Sea. Remote Sensing, 12(3), 480. https://doi.org/10.3390/rs12030480
• Ji, C., Zhang, Y., Cheng, Q., & Tsou, J. Y. (2021). Investigating ocean surface responses to typhoons using reconstructed satellite data. International Journal of Applied Earth Observation and Geoinformation, 103, 102474. https://doi.org/10.1016/j.jag.2021.102474
• Jung, S., Yoo, C., & Im, J. (2022). High-Resolution Seamless Daily Sea Surface Temperature Based on Satellite Data Fusion and Machine Learning over Kuroshio Extension. Remote Sensing, 14(3), 575. https://doi.org/10.3390/rs14030575
• Luo, X., Song, J., Guo, J., Fu, Y., Wang, L. & Cai, Y. (2022). Reconstruction of chlorophyll-a satellite data in Bohai and Yellow sea based on DINCAE method International. Journal of Remote Sensing, 43, 3336-3358. https://doi.org/10.1080/01431161.2022.2090872

Thank you for citing relevant previous work in DINCAE if you make a scientific publication. A bibtex entry can be generated from the DOI by using for example curl -LH "Accept: application/x-bibtex" 'https://doi.org/10.5194/gmd-15-2183-2022'.

Feel free to add your publications by making a pull request or opening an issue.