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Building damage assessment for rapid disaster response with a deep object-based semantic change detection framework: from natural disasters to man-made disasters
Deep object-based semantic change detection framework (ChangeOS) is proposed.
ChangeOS seamlessly integrates object-based image analysis and deep learning.
City-scale building damage assessment can be achieved within one minute.
A global-scale dataset is used to evaluate the effectiveness of ChangeOS.
Two local-scale datasets are used to show its great generalization ability.
Getting Started
Installation
pip install changeos
Requirements:
pytorch == 1.10.0
python >=3.6
skimage
Pillow
Usage
# changeos has four APIs# (e.g., 'list_available_models', 'from_name', 'visualize', 'demo_data')importchangeos# constructing ChangeOS model# support 'changeos_r18', 'changeos_r34', 'changeos_r50', 'changeos_r101'model=changeos.from_name('changeos_r101') # take 'changeos_r101' as example# load your data or our prepared demo data# numpy array of shape [1024, 1024, 3], [1024, 1024, 3]pre_disaster_image, post_disaster_image=changeos.demo_data()
# model inferenceloc, dam=model(pre_disaster_image, post_disaster_image)
# put color map on raw predictionloc, dam=changeos.visualize(loc, dam)
# visualize by matplotlibimportmatplotlib.pyplotaspltplt.subplot(121)
plt.imshow(loc)
plt.subplot(122)
plt.imshow(dam)
plt.show()
Citation
If you use ChangeOS in your research, please cite the following paper:
@article{zheng2021building,
title={Building damage assessment for rapid disaster response with a deep object-based semantic change detection framework: from natural disasters to man-made disasters},
author={Zheng, Zhuo and Zhong, Yanfei and Wang, Junjue and Ma, Ailong and Zhang, Liangpei},
journal={Remote Sensing of Environment},
volume={265},
pages={112636},
year={2021},
publisher={Elsevier}
}
About
ChangeOS: Building damage assessment via Deep Object-based Semantic Change Detection - (RSE 2021)