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

arXiv: https://arxiv.org/abs/2604.14044 · HTML: https://mlsanity.com/paper/2604.14044/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Embodied and 3D vision-language models, one of 16 directions in Multimodal LLMs / vision-language models.
- Builds on 21 of its 58 references that are in the map.
- First posted on arXiv 15 Apr 2026.

## Placed in

- Multimodal LLMs / vision-language models / [Embodied and 3D vision-language models](https://mlsanity.com/mllm/embodied-and-3d-vision-language-models/)

## Builds on (most cited first, 21 of 21)

- [Qwen3 Technical Report](https://mlsanity.com/paper/2505.09388/) (2025)
- [Qwen2.5-VL Technical Report](https://mlsanity.com/paper/2502.13923/) (2025)
- [GPT-4o System Card](https://mlsanity.com/paper/2410.21276/) (2024)
- [Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities](https://mlsanity.com/paper/2507.06261/) (2025)
- [InternVL3: Exploring Advanced Training and Test-Time Recipes for Open-Source Multimodal Models](https://mlsanity.com/paper/2504.10479/) (2025)
- [Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling](https://mlsanity.com/paper/2412.05271/) (2024)
- [InternLM2 Technical Report](https://mlsanity.com/paper/2403.17297/) (2024)
- [TEOChat: A Large Vision-Language Assistant for Temporal Earth Observation Data](https://mlsanity.com/paper/2410.06234/) (2024)
- [RSUniVLM: A Unified Vision Language Model for Remote Sensing via Granularity-oriented Mixture of Experts](https://mlsanity.com/paper/2412.05679/) (2024)
- [DynamicVL: Benchmarking Multimodal Large Language Models for Dynamic City Understanding](https://mlsanity.com/paper/2505.21076/) (2025)
- [RSCC: A Large-Scale Remote Sensing Change Caption Dataset for Disaster Events](https://mlsanity.com/paper/2509.01907/) (2025)
- [CCExpert: Advancing MLLM Capability in Remote Sensing Change Captioning with Difference-Aware Integration and a Foundational Dataset](https://mlsanity.com/paper/2411.11360/) (2024)
- [LISAT: Language-Instructed Segmentation Assistant for Satellite Imagery](https://mlsanity.com/paper/2505.02829/) (2025)
- [GeoEyes: On-Demand Visual Focusing for Evidence-Grounded Understanding of Ultra-High-Resolution Remote Sensing Imagery](https://mlsanity.com/paper/2602.14201/) (2026)
- [UniChange: Unifying Change Detection with Multimodal Large Language Model](https://mlsanity.com/paper/2511.02607/) (2025)
- [BTCChat: Advancing Remote Sensing Bi-temporal Change Captioning with Multimodal Large Language Model](https://mlsanity.com/paper/2509.05895/) (2025)
- [GeoRSMLLM: A Multimodal Large Language Model for Vision-Language Tasks in Geoscience and Remote Sensing](https://mlsanity.com/paper/2503.12490/) (2025)
- [Evolving, Not Training: Zero-Shot Reasoning Segmentation via Evolutionary Prompting](https://mlsanity.com/paper/2512.24702/) (2025)
- [GeoAlignCLIP: Enhancing Fine-Grained Vision-Language Alignment in Remote Sensing via Multi-Granular Consistency Learning](https://mlsanity.com/paper/2603.09566/) (2026)
- [ReasonCD: A Multimodal Reasoning Large Model for Implicit Change-of-Interest Semantic Mining](https://mlsanity.com/paper/2512.19354/) (2025)
- [bi-modal textual prompt learning for vision-language models in remote sensing](https://mlsanity.com/paper/2601.20675/) (2026)

## Built on by (most cited first, 1 of 1)

- [TerraAgent: Demonstration-Grounded Multi-Agent Reasoning for Long-Context Remote Sensing Visual Question Answering](https://mlsanity.com/paper/2610.12517/) (2026)

## Abstract (arXiv)

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.