Decoding the Delta: Unifying Remote Sensing Change Detection and Understanding with Multimodal Large Language Models

• URL: http://arxiv.org/abs/2604.14044v1
• Date: Wed, 15 Apr 2026 16:23:05 GMT
• Title: Decoding the Delta: Unifying Remote Sensing Change Detection and Understanding with Multimodal Large Language Models
• Abstract summary: We present Delta-LLaVA, a novel MLLM framework specifically tailored for multi-temporal remote sensing interpretation.<n>It overcomes the limitations of naive feature concatenation through three core innovations.<n>It decisively outperforms leading generalist MLLMs and specialized segmentation models in complex change deduction and high-precision boundary localization.
• Score: 9.373908834130669
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: While Multimodal Large Language Models (MLLMs) excel in general vision-language tasks, their application to remote sensing change understanding is hindered by a fundamental "temporal blindness". Existing architectures lack intrinsic mechanisms for multi-temporal contrastive reasoning and struggle with precise spatial grounding. To address this, we first introduce Delta-QA, a comprehensive benchmark comprising 180k visual question-answering samples. Delta-QA unifies pixel-level segmentation and visual question answering across bi- and tri-temporal scenarios, structuring change interpretation into four progressive cognitive dimensions. Methodologically, we propose Delta-LLaVA, a novel MLLM framework explicitly tailored for multi-temporal remote sensing interpretation. It overcomes the limitations of naive feature concatenation through three core innovations: a Change-Enhanced Attention module that systematically isolates and amplifies visual differences, a Change-SEG module utilizing Change Prior Embedding to extract differentiable difference features as input for the LLM, and Local Causal Attention to prevent cross-temporal contextual leakage. Extensive experiments demonstrate that Delta-LLaVA decisively outperforms leading generalist MLLMs and specialized segmentation models in complex change deduction and high-precision boundary localization, establishing a unified framework for earth observation intelligence.

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