{"id":13785218,"url":"https://github.com/Z-Zheng/ChangeOS","last_synced_at":"2025-05-11T20:32:33.529Z","repository":{"id":41405186,"uuid":"397239586","full_name":"Z-Zheng/ChangeOS","owner":"Z-Zheng","description":"ChangeOS: Building damage assessment via Deep Object-based Semantic Change Detection - (RSE 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Learning"],"readme":"\n\u003ch3 align=\"center\"\u003eBuilding damage assessment for rapid disaster response with a deep object-based semantic change detection framework:\u003cbr\u003efrom natural disasters to man-made disasters\u003c/h3\u003e\n\n\n\u003ch5 align=\"right\"\u003eby \u003ca href=\"http://zhuozheng.top/\"\u003eZhuo Zheng\u003c/a\u003e, \u003ca href=\"http://rsidea.whu.edu.cn/\"\u003eYanfei Zhong\u003c/a\u003e, \u003ca href=\"https://junjue-wang.github.io/homepage/\"\u003eJunjue Wang\u003c/a\u003e, Ailong Ma and \u003ca href=\"http://www.lmars.whu.edu.cn/prof_web/zhangliangpei/rs/index.html\"\u003eLiangpei Zhang\u003c/a\u003e\u003c/h5\u003e\n\n[[`Paper`]](https://www.sciencedirect.com/science/article/pii/S0034425721003564) [[`BibTeX`](#Citation)]\n\n\u003cdiv align=\"center\"\u003e\n  \u003cimg src=\"https://raw.githubusercontent.com/Z-Zheng/images_repo/master/ChangeOS%400%2C25x.png\"\u003e\u003cbr\u003e\u003cbr\u003e\n\u003c/div\u003e\n\n\nThis 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).\n\n\n---------------------\n\n## Highlights\n\n- Deep object-based semantic change detection framework (ChangeOS) is proposed.\n- ChangeOS seamlessly integrates object-based image analysis and deep learning.\n- City-scale building damage assessment can be achieved within one minute.\n- A global-scale dataset is used to evaluate the effectiveness of ChangeOS.\n- Two local-scale datasets are used to show its great generalization ability.\n\n\n\n## Getting Started\n### Installation\n\n```bash\npip install changeos\n```\n\n#### Requirements:\n- pytorch == 1.10.0\n- python \u003e=3.6\n- skimage\n- Pillow\n\n### Usage\n\n```python\n# changeos has four APIs\n# (e.g., 'list_available_models', 'from_name', 'visualize', 'demo_data')\nimport changeos\n\n\n# constructing ChangeOS model\n# support 'changeos_r18', 'changeos_r34', 'changeos_r50', 'changeos_r101'\nmodel = changeos.from_name('changeos_r101') # take 'changeos_r101' as example\n\n# load your data or our prepared demo data\n# numpy array of shape [1024, 1024, 3], [1024, 1024, 3]\npre_disaster_image, post_disaster_image = changeos.demo_data()\n\n# model inference\nloc, dam = model(pre_disaster_image, post_disaster_image)\n\n# put color map on raw prediction\nloc, dam = changeos.visualize(loc, dam)\n\n# visualize by matplotlib\nimport matplotlib.pyplot as plt\nplt.subplot(121)\nplt.imshow(loc)\nplt.subplot(122)\nplt.imshow(dam)\nplt.show()\n\n```\n\n\n\n## \u003ca name=\"Citation\"\u003e\u003c/a\u003eCitation\nIf you use ChangeOS in your research, please cite the following paper:\n```text\n@article{zheng2021building,\n  title={Building damage assessment for rapid disaster response with a deep object-based semantic change detection framework: from natural disasters to man-made disasters},\n  author={Zheng, Zhuo and Zhong, Yanfei and Wang, Junjue and Ma, Ailong and Zhang, Liangpei},\n  journal={Remote Sensing of Environment},\n  volume={265},\n  pages={112636},\n  year={2021},\n  publisher={Elsevier}\n}\n```","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FZ-Zheng%2FChangeOS","html_url":"https://awesome.ecosyste.ms/projects/github.com%2FZ-Zheng%2FChangeOS","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FZ-Zheng%2FChangeOS/lists"}