Real-World Knowledge-Guided Change Data Synthesis for Remote Sensing

2026-08-25

AI Summary

为解决变化检测模型训练数据不足的问题，提出KnowChange框架，利用预训练视觉-语言模型作为知识源，生成多样化的变化数据。

Abstract

Change data synthesis provides a cost-effective solution for expanding training data and improving the performance of change detection models. However, existing synthesis methods typically rely on handcrafted rules to simulate changes, where limited coverage of class transitions restricts the diversity of synthesized data, while predefined transition designs limit their flexibility in accommodating varied change types. In this work, we introduce KnowChange, a knowledge-guided change data synthesis framework that leverages pretrained vision-language models as knowledge sources to reason about plausible change locations and class transitions from pre-change scenes and desired change types. By integrating knowledge-guided change simulation with generalizable synthesis models, KnowChange enables flexible synthesis of diverse change types within a unified framework. Extensive experiments demonstrate that KnowChange-generated data consistently outperforms existing synthetic datasets in both synthetic-to-real transfer and synthetic data augmentation, despite being generated at a compact scale. Further analyses show that the knowledge-guided change simulation can be seamlessly integrated into existing synthesis pipelines and enhance the downstream utility of synthesized data.

Problem

• change data synthesis
• handcrafted rules
• class transitions
• synthetic data diversity
• flexibility

Innovation

• knowledge-guided change data synthesis
• vision-language models
• flexible synthesis of diverse change types
• synthetic-to-real transfer