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 is a comprehensive, open-source toolbox that integrates numerous change detection methods, supporting research and development with benchmarking, data analysis, and algorithm descriptions to advance the change detection community.
Contribution
It introduces the most complete change detection toolbox combining various methods, modules, and benchmarking tools in a unified platform for the first time.
Findings
Benchmarking of different change detection methods
Support for a wide range of algorithms and modules
Facilitates reimplementation and development of new change detectors
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…
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Taxonomy
TopicsTime Series Analysis and Forecasting
MethodsSparse Evolutionary Training
