WAN: Weakly-Supervised Domain Adaptation for Built-up Region Segmentation in Aerial and Satellite Imagery
By Javed Iqbal and Mohsen Ali
- 2020.12.09: code released for LT-WAN and OS-WAN
This repository contains the weakly supervised learning framwork for domain adaptation of built-up regions segmnentation based on the work described in ISPRS Photogrametery and Remote Sensing 2020 paper "[WAN: Weakly-Supervised Domain Adaptation for Built-up Region Segmentation in Aerial and Satellite Imagery]". (https://arxiv.org/pdf/2007.02277.pdf).
The code is tested in Ubuntu 16.04. It is implemented based on Keras with tensorflow backend and Python 3.5. For GPU usage, the maximum GPU memory consumption is about 7 GB in a single GTX 1080.
We assume you are working in wan-master folder.
- Datasets:
- Download Rwanda dataset.
- Put downloaded data in "datasets" folder.
- Set the PYTHONPATH environment variable:
cd wan-master
- Adaptation
- OSA: Output space Adaptation:
python adapt_OSA.py --data-dir path_to_dataset_folder --data-list-train training_images_list --data-list-val validation_images_list
- LTA: Output space Adaptation:
python adapt_LTA.py --data-dir path_to_dataset_folder --data-list-train training_images_list --data-list-val validation_images_list
- To run the code, you need to set the data paths of source data (data-root) and target data (data-root-tgt) by yourself. Besides that, you can keep other argument setting as default.
- Evaluation
- Train in source domain
python train.py --data-dir path_to_dataset_folder --data-list-train training_images_list --data-list-val validation_images_list
If you found this useful, please cite our paper.
@inproceedings{iqbal2020weakly,
title={Weakly-supervised domain adaptation for built-up region segmentation in aerial and satellite imagery},
author={Iqbal, Javed and Ali, Mohsen},
journal={ISPRS Journal of Photogrammetry and Remote Sensing}, volume={167}, pages={263--275}, year={2020}, publisher={Elsevier} }
Contact: [email protected]