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Weakly-Supervised Domain Adaptation for Built-up Region Segmentation in Aerial and Satellite Imagery

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WAN: Weakly-Supervised Domain Adaptation for Built-up Region Segmentation in Aerial and Satellite Imagery

ISPRS Journal of Photogrametery and Remote Sensing

By Javed Iqbal and Mohsen Ali

Update

  • 2020.12.09: code released for LT-WAN and OS-WAN

Contents

  1. Introduction
  2. Requirements
  3. Setup
  4. Usage
  5. Results
  6. Note
  7. Citation

Introduction

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).

Requirements:

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.

Setup

We assume you are working in wan-master folder.

  1. Datasets:
  • Download Rwanda dataset.
  • Put downloaded data in "datasets" folder.

Usage

  1. Set the PYTHONPATH environment variable:
cd wan-master

  1. 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.
  1. Evaluation
  1. 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

Citation:

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]

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