  Monitoring changes in the Earth's surface is crucial for understanding
natural processes and human impacts, necessitating precise and comprehensive
interpretation methodologies. Remote sensing satellite imagery offers a unique
perspective for monitoring these changes, leading to the emergence of remote
sensing image change interpretation (RSICI) as a significant research focus.
Current RSICI technology encompasses change detection and change captioning,
each with its limitations in providing comprehensive interpretation. To address
this, we propose an interactive Change-Agent, which can follow user
instructions to achieve comprehensive change interpretation and insightful
analysis, such as change detection and change captioning, change object
counting, change cause analysis, etc. The Change-Agent integrates a multi-level
change interpretation (MCI) model as the eyes and a large language model (LLM)
as the brain. The MCI model contains two branches of pixel-level change
detection and semantic-level change captioning, in which the BI-temporal
Iterative Interaction (BI3) layer is proposed to enhance the model's
discriminative feature representation capabilities. To support the training of
the MCI model, we build the LEVIR-MCI dataset with a large number of change
masks and captions of changes. Experiments demonstrate the SOTA performance of
the MCI model in achieving both change detection and change description
simultaneously, and highlight the promising application value of our
Change-Agent in facilitating comprehensive interpretation of surface changes,
which opens up a new avenue for intelligent remote sensing applications. To
facilitate future research, we will make our dataset and codebase of the MCI
model and Change-Agent publicly available at
https://github.com/Chen-Yang-Liu/Change-Agent
