Pytorch -- Multitemporal Land Cover Classification Network

A (yet barebone) Pytorch port of Rußwurm & Körner (2018) Tensorflow implementation

Please consider citing

Rußwurm M., Körner M. (2018). Multi-Temporal Land Cover Classification with
Sequential Recurrent Encoders. ISPRS International Journal of Geo-Information, 2018.

if you use this repository

Activations while encoding sequence:

Dependencies

Python dependencies

pip install numpy
pip install pandas>=0.23.4
pip install visdom==0.1.8.4
pip install rasterio>=1.0.2

# install pytorch 0.4.1 (https://pytorch.org/)
pip3 install torch torchvision

Download dataset to src/data and model checkpoint to src/checkpoints

bash download.sh

Train 10 epochs (batchsize 16, dataloader-workers 16) with initialized weights from checkpoint file checkpoints/model_00.pth

# add src folder to python path
export PYTHONPATH="$PATHONPATH:$PWD/src"

# train
python src/train.py data -b 16 -w 16 -s checkpoints/model_00.pth

Visdom Support

Start visdom server in screen, tmux or other terminal with $ visdom and open http://localhost:8097 in the browser while training

Comparison to Tensorflow implementation

not yet implemented features compared to the Tensorflow version

ConvGRU integration in train.py
bidirectional RNN loop
masking of the background class

Source of ConvLSTM and ConvGRU implementations