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Open-CD: A Comprehensive Toolbox for Change Detection

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arxiv 2407.15317 v2 pith:3E7IBVF7 submitted 2024-07-22 cs.CV

classification cs.CV
keywords changetoolboxdetectionmethodsopen-cdreportcommunitycomponents
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Compress-Align-Detect: onboard change detection from unregistered images

    cs.CV 2025-07 conditional novelty 7.0 of 10

    A single neural network performs compression, co-registration, and change detection onboard a satellite, achieving F1 up to about 70% at low bitrates on simulated unregistered image pairs.

  2. RS-MTDF: Multi-Teacher Distillation and Fusion for Remote Sensing Semi-Supervised Semantic Segmentation

    cs.CV 2025-06 conditional novelty 5.0 of 10

    A multi-teacher framework distills frozen DINOv2 and CLIP features into a student model to improve low-label remote sensing segmentation.

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