Unlocking Multi-Spectral Data for Multi-Modal Models with Guided Inputs and Chain-of-Thought Reasoning

2026-04-22

AI Summary

This work addresses the limitation of general-purpose multimodal large language models, which typically support only RGB imagery and struggle to directly interpret multispectral remote sensing data. The authors propose a training-free, inference-stage adaptation method that enables off-the-shelf models such as Gemini 2.5 to achieve zero-shot understanding of multispectral inputs. This is accomplished through a mapping from multispectral to RGB visual space, domain-knowledge-informed prompt engineering, and a chain-of-thought reasoning mechanism. Without any model fine-tuning, the approach effectively leverages specialized sensor data and substantially improves zero-shot performance on established remote sensing benchmarks. The results demonstrate, for the first time, the feasibility and potential of transferring general multimodal models to the remote sensing domain across modalities.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Computer Vision: Multi-modal Vision
• Natural Language Processing: Language Grounding & Multi-modal NLP

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Multi-spectral imagery is a valuable input signal for Remote Sensing applications, such as land-use and land-cover classification and environmental monitoring. However, generalist Large Multi-modal Models (LMMs) are typically trained on RGB images, limiting their applicability to the RGB domain. At the same time, training multi-spectral multi-modal models is expensive and produces uniquely specialized models. To address this, we propose a novel training-free approach that introduces multi-spectral data within the inference pipeline of standard RGB-only LMMs, allowing large gains in performance. Our approach leverages the LMMs' understanding of the visual space by adapting non-RGB inputs to that space and injecting domain-specific information and Chain-of-Thought reasoning as instructions. We demonstrate this with the Gemini 2.5 model and observe strong Zero-Shot performance gains on popular Remote Sensing benchmarks. These results highlight the potential for geospatial professionals to leverage powerful generalist models for specialized sensor inputs, benefiting from rich reasoning capabilities grounded in specialized data.

Problem

• multi-spectral imagery
• large multi-modal models
• remote sensing
• RGB domain
• specialized sensor inputs

Innovation

• multi-spectral imagery
• large multi-modal models
• training-free adaptation
• chain-of-thought reasoning
• zero-shot remote sensing