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Data-driven gap filling of satellite image time series using neural networks with spatiotemporal partial convolutions

This repository implements three-dimensional partial convolutions for filling gaps in satellite image time series. The repository includes

  • Code for three-dimensional partial convolututional layers (extending tensorflow.keras.layers.Conv3D)
  • Code for a U-Net-like model to fill gaps of spatiotemporal blocks
  • A small test dataset based on Sentinel-5P total column carbon monoxide measurements
  • An example model to fill gaps in the test dataset

The method is based on the following paper on two-dimensional image inpainting:

Liu, G., Reda, F. A., Shih, K. J., Wang, T. C., Tao, A., & Catanzaro, B. (2018). Image inpainting for irregular holes using partial convolutions. In Proceedings of the European conference on computer vision (ECCV) (pp. 85-100).

The code is based on the existing Keras implementation of two-dimensional partial convolutions copyright (c) 2018 Mathias Gruber, licensed under the terms of the MIT license.

Dependencies

  • Python 3
  • TensorFlow >= 2.5.0
  • RasterIO
  • NumPy
  • scikit-image

Example

The figure below shows input (top row) and correspodning predictions (bottom row) of the provided example model, applied on a small spatiotemporal block of Sentinel-5P total column carbon monoxide measurements.

Getting started

If all dependcies are available, you can directly run one of the provided scripts to train a model on provided test data (01_train.py) or to fill gaps to fill gaps in provided test data using the pre-trained model (02_predict.py).

Docker

In case you have trouble with dependencies and or version conflicts of packages, you can build the provided Docker image, which gives you a JupyterLab environment with pre-installed dependencies:

sudo docker build -t appelmar/stpconv .
sudo docker run -it --rm -p 8888:8888 -e JUPYTER_ENABLE_LAB=yes -v ${PWD}:/home/jovyan/work appelmar/stpconv

This should give a link to access JupyterLab in your browser und run the provided scripts.

Data Format and Naming Conventions

Input and output data blocks are stored as GeoTIFF files, where bands represent time. Notice the following file naming conventions: GeoTIFF files starting with X represent input measurements for training, where artificial gaps have been added. Files starting with Y represent true measurements without artificially added gaps (but still containing gaps in many cases). Binary masks of input data where all pixels with valid measurements are 1 and others 0 are stored in files whose name starts with MASK, while files starting with VALMASK contain a binary mask where only pixels that are available in Y but not in X are 1. The latter is used for validation on artificially removed pixels only. Numbers in filenames encode spatial and temporal block indexes.

Original data

A larger training and validation dataset has been published at https://zenodo.org/record/6838652#.YtE92dJBwUE.