Building damage assessment for rapid disaster response with a deep object-based semantic change detection framework:
from natural disasters to man-made disasters
by Zhuo Zheng, Yanfei Zhong, Junjue Wang, Ailong Ma and Liangpei Zhang
This is an official implementation of ChangeOS in our RSE 2021 paper 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.
pip install changeos- pytorch == 1.10.0
- python >=3.6
- skimage
- Pillow
# changeos has four APIs
# (e.g., 'list_available_models', 'from_name', 'visualize', 'demo_data')
import changeos
# 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 inference
loc, dam = model(pre_disaster_image, post_disaster_image)
# put color map on raw prediction
loc, dam = changeos.visualize(loc, dam)
# visualize by matplotlib
import matplotlib.pyplot as plt
plt.subplot(121)
plt.imshow(loc)
plt.subplot(122)
plt.imshow(dam)
plt.show()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}
}
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