https://github.com/Z-Zheng/ChangeOS

ChangeOS: Building damage assessment via Deep Object-based Semantic Change Detection - (RSE 2021)
https://github.com/Z-Zheng/ChangeOS

building-damage-assessment change-detection deep-learning object-based-image-analysis remote-sensing

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ChangeOS: Building damage assessment via Deep Object-based Semantic Change Detection - (RSE 2021)

• Host: GitHub
• URL: https://github.com/Z-Zheng/ChangeOS
• Owner: Z-Zheng
• Created: 2021-08-17T12:03:34.000Z (about 3 years ago)
• Default Branch: master
• Last Pushed: 2021-12-21T08:24:42.000Z (almost 3 years ago)
• Last Synced: 2024-04-29T11:45:45.207Z (7 months ago)
• Topics: building-damage-assessment, change-detection, deep-learning, object-based-image-analysis, remote-sensing
• Language: Python
• Homepage:
• Size: 6.84 KB
• Stars: 63
• Watchers: 6
• Forks: 13
• Open Issues: 1
• Metadata Files:
  • Readme: README.md
  • Changelog: changeos/__init__.py

Awesome Lists containing this project

• awesome-remote-sensing-change-detection - Zheng Z, Zhong Y, Wang J, et al. Building damage assessment for rapid disaster response with a deep object-based semantic change detection framework: from natural disasters to man-made disasters

README

        

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



[[`Paper`]](https://www.sciencedirect.com/science/article/pii/S0034425721003564) [[`BibTeX`](#Citation)]



  





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](https://www.sciencedirect.com/science/article/pii/S0034425721003564).

---------------------

## Highlights

- 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

```bash

pip install changeos

```

#### Requirements:

- pytorch == 1.10.0

- python >=3.6

- skimage

- Pillow

### Usage

```python

# 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()

```

## Citation

If you use ChangeOS in your research, please cite the following paper:

```text

@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}

}

```