awesome-remote-sensing-change-detection and Change-Detection-Review
These are **complements** — one is a broad curated index of datasets, tools, methods, and competitions across the change-detection field, while the other is a focused review paper with accompanying implementation code and datasets for deep learning approaches, making them useful together for comprehensive and specialized understanding.
About awesome-remote-sensing-change-detection
wenhwu/awesome-remote-sensing-change-detection
A comprehensive and up-to-date compilation of datasets, tools, methods, review papers, and competitions for remote sensing change detection.
Organizes datasets across multiple imaging modalities (optical, SAR, multi-modal) with standardized metadata including resolution, spatial extent, and class taxonomies, enabling systematic benchmarking of change detection algorithms. Categorizes methods by architecture type—foundation models, diffusion/GAN-based, transformers, and traditional approaches—linking each to source code and peer-reviewed publications. Includes specialized resources for disaster response applications and actively tracks competition benchmarks alongside curated review papers to support algorithm development and evaluation.
About Change-Detection-Review
MinZHANG-WHU/Change-Detection-Review
A review of change detection methods, including codes and open data sets for deep learning. From paper: change detection based on artificial intelligence: state-of-the-art and challenges.
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