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.
This compilation helps professionals working with satellite imagery detect changes on the Earth's surface. It provides a curated list of datasets, tools, and methods for tasks like monitoring urban growth, assessing disaster damage, or tracking land cover shifts. It's designed for remote sensing scientists, urban planners, environmental analysts, and disaster response teams.
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.
This project provides a comprehensive overview of how artificial intelligence can be used to detect changes on the Earth's surface. It helps urban planners, environmental monitors, and disaster assessment teams analyze remote sensing images like satellite photos or radar data to identify significant alterations. It takes in various types of remote sensing data and outlines the best AI methods, code examples, and open datasets to pinpoint these changes effectively.
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