Open-CD: A Comprehensive Toolbox for Change Detection
Kaiyu Li, Jiawei Jiang, Andrea Codegoni, Chengxi Han, Yupeng Deng, Keyan Chen, Zhuo Zheng, Hao Chen, Ziyuan Liu, Yuantao Gu, Zhengxia Zou, Zhenwei Shi, Sheng Fang, Deyu Meng, Zhi Wang, Xiangyong Cao
TL;DR
Open-CD tackles the need for a unified, high-quality toolbox for pixel-level bi-temporal change detection in remote sensing. It leverages OpenMMLab toolkits to provide a modular, config-driven platform that supports a wide range of methods, a model zoo, training/inference pipelines, and data-analysis scripts. Through benchmarking on LEVIR-CD, the paper demonstrates competitive or improved $F_1^c$ and $IoU^c$ metrics across methods and highlights performance gains over official implementations, validating the platform's reliability and efficiency. Overall, Open-CD aims to accelerate research, reimplementation, and competition in change detection by enabling flexible construction, deployment, and downstream validation for foundation and generative models.
Abstract
We present Open-CD, a change detection toolbox that contains a rich set of change detection methods as well as related components and modules. The toolbox started from a series of open source general vision task tools, including OpenMMLab Toolkits, PyTorch Image Models, etc. It gradually evolves into a unified platform that covers many popular change detection methods and contemporary modules. It not only includes training and inference codes, but also provides some useful scripts for data analysis. We believe this toolbox is by far the most complete change detection toolbox. In this report, we introduce the various features, supported methods and applications of Open-CD. In addition, we also conduct a benchmarking study on different methods and components. We wish that the toolbox and benchmark could serve the growing research community by providing a flexible toolkit to reimplement existing methods and develop their own new change detectors. Code and models are available at https://github.com/likyoo/open-cd. Pioneeringly, this report also includes brief descriptions of the algorithms supported in Open-CD, mainly contributed by their authors. We sincerely encourage researchers in this field to participate in this project and work together to create a more open community. This toolkit and report will be kept updated.