BTCChat: Advancing Remote Sensing Bi-temporal Change Captioning with Multimodal Large Language Model

• URL: http://arxiv.org/abs/2509.05895v1
• Date: Sun, 07 Sep 2025 02:16:18 GMT
• Title: BTCChat: Advancing Remote Sensing Bi-temporal Change Captioning with Multimodal Large Language Model
• Authors: Yujie Li, Wenjia Xu, Yuanben Zhang, Zhiwei Wei, Mugen Peng,
• Abstract summary: Bi-temporal satellite imagery supports critical applications such as urban development monitoring and disaster assessment.<n>Previous methods process image pairs through direct concatenation, inadequately modeling temporal correlations and spatial semantic changes.<n>We propose BTCChat, a multi-temporal MLLM with advanced bi-temporal change understanding capability.
• Score: 24.844748050706468
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Bi-temporal satellite imagery supports critical applications such as urban development 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.

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