Open-CD: A Comprehensive Toolbox for Change Detection

2024-07-22

arXiv.org

AI Summary

The remote sensing change detection community lacks a unified, scalable, and extensible toolkit. Method: This paper introduces the first open-source, modular, and standardized toolbox built on PyTorch and the OpenMMLab ecosystem. It systematically integrates over 20 state-of-the-art models—including bi-temporal feature alignment, difference modeling, and self-supervised pretraining—and provides end-to-end training/inference pipelines, visualization tools, and comprehensive data preprocessing scripts. Contributions/Results: (1) A unified API for multi-paradigm algorithms, accompanied by author-provided conceptual explanations; (2) The first standardized benchmarking framework, delivering reproducible performance reports and pretrained weights on WHU-CD, LEVIR-CD, and other benchmarks; (3) Enhanced reproducibility, fair model comparison, and streamlined development of novel methods—facilitating collaborative community advancement. The codebase is publicly available and actively maintained.

Technology Category

• Computer Vision: Remote Sensing / Geospatial AI
• Machine Learning: Large Multimodal Models (LMMs)
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Web Mining and Content Analysis: Large pretrained models with web data

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.

Problem

• Develop a unified toolbox for change detection methods
• Provide training, inference, and data analysis scripts
• Benchmark and support diverse change detection algorithms

Innovation

• Unified platform for change detection methods
• Includes training, inference, and data analysis
• Open-source toolbox with benchmarking study