Awesome

Presentation

This repository contains the code of our paper: DIAL: Deep Interactive and Active Learning for Semantic Segmentation in Remote Sensing.

To use

Prepare data

The training datasets should be stored in a folder MyDataset organized as follows:

• a folder named imgs containing the RGB images.
• a folder named gts containing the ground-truths.

:warning: Ground-truth files must have the same names than their associated image.

Example for ISPRS Potsdam dataset.

cd <PotsdamDataset>
sudo apt install rename
cd gts; rename 's/_label//' *; cd ../imgs; rename 's/_RGB//' *

The ground-truth maps have to be one-hot encoded (i.e. not in a RGB format):

cd ICSS
python preprocess/format_gt.py -n 6 -d <PathToMyDataset>/gts

To train:

All parameters (including the activation of DISIR and DISCA) can be set in a config file similar to configs/some_config.yml.

In src/train.py: Train a model on the train set and test it on the evaluation set (with N clicks simulations if DISIR is enabled). It is possible to skip the training with a pretrained model.

Active learning

Pixelwise AL

python -m src.active_learning.pixelwise_al -d /data/gaston/Potsdam -c configs/some_config.yml  -p data/models/my_model.pt

Patchwise AL

python -m src.active_learning.patchwise_al -d /data/gaston/Potsdam -c configs/some_config.yml  -p data/models/my_model.pt

Licence

Code is released under the MIT license for non-commercial and research purposes only. For commercial purposes, please contact the authors.

See LICENSE for more details.

Acknowledgements

This work has been jointly conducted at Alteia and ONERA-DTIS.