A method to perform unsupervised cross-region adaptation of crop classifiers trained with satellite image time series.

Joachim Nyborg

Last update: Nov 1, 2022

Related tags

Deep Learning timematch

Overview

TimeMatch

Official source code of TimeMatch: Unsupervised Cross-region Adaptation by Temporal Shift Estimation by Joachim Nyborg, Charlotte Pelletier, Sébastien Lefèvre, and Ira Assent.

Requirements

Python requirements

Python 3.9.4, PyTorch 1.8.1, and more in environment.yml.

TimeMatch dataset download

The dataset can be freely downloaded from Zenodo. The extracted size is about 78 GB.

Pre-trained models and full results

Pre-trained models and results can also be downloaded from Zenodo

Usage

Setup conda environment and activate

conda env create -f environment.yml
conda activate timematch

Download dataset and extract to /media/data/timematch_data (or set --data_root to its path for train.py).

Pre-trained models should be extracted to timematch/outputs.

Example: train model on the source domain

python train.py -e pseltae_32VNH --source denmark/32VNH/2017 --target denmark/32VNH/2017

Train TimeMatch with pre-trained model

python train.py -e timematch_32VNH_to_30TXT --source denmark/32VNH/2017 --target france/30TXT/2017 timematch --weights outputs/pseltae_32VNH

All training scripts can be found in the scripts directory.

Reference

TODO

Credits

The implementation of PSE+LTAE is based on the official implementation
The implementation of competitors MMD, DANN, and CDAN is based on Transfer Learning Library, and JUMBOT on its official implementation.
The annotations used in the TimeMatch dataset from the LPIS produced by the mapping agencies IGN (France), NaturErhverstyrelsen (Denmark), and the AMA (Austria).
The Sentinel-2 imagery were accessed from the AWS bucket managed by Sinergise

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A method to perform unsupervised cross-region adaptation of crop classifiers trained with satellite image time series.

Related tags

Overview

TimeMatch

Requirements

Python requirements

TimeMatch dataset download

Pre-trained models and full results

Usage

Reference

Credits

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