Foundation Models for Remote Sensing Change Detection: Paradigm transformation and future opportunities

Change detection (CD) is an essential task in the field of remote sensing (RS), supporting applications such as urban management, disaster monitoring, resource surveys, and environmental assessment. CD involves cross-temporal feature alignment, consistency modeling, change discrimination, and semantic reasoning while also requiring robust and generalizable representations across multisource and multiscale data. With the rapid advancement of foundation models (FMs), large-scale pretrained models have demonstrated significant advantages in representation capability, cross-scene transferability, and data efficiency. These advances provide new opportunities for advancing research in CD tasks. This survey focuses on the emerging field of FMs for CD (FM4CD). This article outlines the background and motivation for incorporating FMs into CD tasks. Then, we systematically overview representative CD methods from the perspectives of FMs, summarizing their characteristics and advantages in change modeling and semantic understanding. Subsequently, this article analyzes key factors in FMs for CD tasks, including dataset scale, adaptation strategies, and generalization capabilities. This article aims to provide researchers with a systematic analysis to advance the development of FMs for RSCD, thereby driving the expansion of CD technology into complex and diverse application scenarios. We will continuously track and update related works at https://github.com/Jscript10/Awesome-FM4CD.