BTCCHAT: ADVANCING REMOTE SENSING BI-TEMPORAL CHANGE CAPTIONING WITH MULTIMODAL LARGE LANGUAGE MODEL
Yujie Li, Yuanben Zhang, Zhiwei Wei, Mugen Peng
Abstract
Bi-temporal satellite imagery supports critical applications such as urbanization monitoring and disaster assessment. Although powerful multimodal large language models~(MLLMs) have been applied in bi-temporal change analysis, previous methods process image pairs through direct concatenation, inadequately modeling temporal correlations and spatial semantic changes. This deficiency hampers visual-semantic alignment in change understanding, thereby constraining the overall effectiveness of current approaches. To address this gap, we propose BTCChat, a multi-temporal MLLM with advanced bi-temporal change understanding capability. BTCChat supports bi-temporal change captioning and retains single-image interpretation capability. To better capture temporal features and spatial semantic changes in image pairs, we design a Change Extraction module. Moreover, to enhance the model's attention to spatial details, we introduce a Prompt Augmentation mechanism, which incorporates contextual clues into the prompt to enhance model performance. Experimental results demonstrate that BTCChat achieves state-of-the-art performance on change captioning and visual question answering tasks. The code is available \href{https://github.com/IntelliSensing/BTCChat}{here}.
BibTeX
@inproceedings{icassp2026_btcchatadvancing,
title = {BTCCHAT: ADVANCING REMOTE SENSING BI-TEMPORAL CHANGE CAPTIONING WITH MULTIMODAL LARGE LANGUAGE MODEL},
author = {Yujie Li and Yuanben Zhang and Zhiwei Wei and Mugen Peng},
booktitle = {ICASSP 2026},
year = {2026}
}