VLRS-Bench: A Vision-Language Reasoning Benchmark for Remote Sensing

2026-02-04

AI Summary

This work addresses the limited evaluation of complex cognitive reasoning in existing multimodal large models for remote sensing, which predominantly focus on perceptual tasks. To bridge this gap, the authors introduce the first benchmark specifically designed for complex vision-language reasoning in remote sensing, structured around three core dimensions: cognition, decision-making, and prediction. The benchmark comprises 14 carefully crafted tasks incorporating spatiotemporal structures with up to eight sequential stages. By integrating domain-specific remote sensing priors, expert annotations, and multi-stage temporal modeling, the authors generate 2,000 high-fidelity, high-complexity question-answer pairs (averaging 71 words each). Empirical evaluation reveals substantial performance limitations of current multimodal large models on advanced remote sensing reasoning tasks, thereby establishing a critical foundation and clear direction for future research.

Technology Category

• Computer Vision: Remote Sensing / Geospatial AI
• Knowledge Representation and Reasoning: Computational Complexity of Reasoning
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

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

Abstract

Recent advancements in Multimodal Large Language Models (MLLMs) have enabled complex reasoning. However, existing remote sensing (RS) benchmarks remain heavily biased toward perception tasks, such as object recognition and scene classification. This limitation hinders the development of MLLMs for cognitively demanding RS applications. To address this, , we propose a Vision Language ReaSoning Benchmark (VLRS-Bench), which is the first benchmark exclusively dedicated to complex RS reasoning. Structured across the three core dimensions of Cognition, Decision, and Prediction, VLRS-Bench comprises 2,000 question-answer pairs with an average length of 71 words, spanning 14 tasks and up to eight temporal phases. VLRS-Bench is constructed via a specialized pipeline that integrates RS-specific priors and expert knowledge to ensure geospatial realism and reasoning complexity. Experimental results reveal significant bottlenecks in existing state-of-the-art MLLMs, providing critical insights for advancing multimodal reasoning within the remote sensing community.

Problem

• remote sensing
• multimodal reasoning
• vision-language benchmark
• cognitive tasks
• MLLMs

Innovation

• Vision-Language Reasoning
• Remote Sensing Benchmark
• Multimodal Large Language Models
• Geospatial Reasoning
• Cognitive Tasks