NeSy-Route: A Neuro-Symbolic Benchmark for Constrained Route Planning in Remote Sensing
Ming Yang, Zhi Zhou, Shi-Yu Tian, Kun-Yang Yu, Lan-Zhe Guo, Yu-Feng Li

TL;DR
NeSy-Route is a large-scale neuro-symbolic benchmark designed to evaluate and improve the planning capabilities of multimodal large language models in remote sensing applications, addressing current evaluation limitations.
Contribution
It introduces an automated data-generation framework and a hierarchical evaluation protocol for comprehensive assessment of perception, reasoning, and planning in remote sensing tasks.
Findings
Existing MLLMs have significant deficiencies in perception and planning.
NeSy-Route includes over 10,000 diverse route-planning samples with optimal solutions.
The benchmark enables detailed analysis of model capabilities across multiple levels.
Abstract
Remote sensing underpins crucial applications such as disaster relief and ecological field surveys, where systems must understand complex scenes and constraints and make reliable decisions. Current remote-sensing benchmarks mainly focus on evaluating perception and reasoning capabilities of multimodal large language models (MLLMs). They fail to assess planning capability, stemming either from the difficulty of curating and validating planning tasks at scale or from evaluation protocols that are inaccurate and inadequate. To address these limitations, we introduce NeSy-Route, a large-scale neuro-symbolic benchmark for constrained route planning in remote sensing. Within this benchmark, we introduce an automated data-generation framework that integrates high-fidelity semantic masks with heuristic search to produce diverse route-planning tasks with provably optimal solutions. This allows…
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Taxonomy
TopicsMultimodal Machine Learning Applications · Automated Road and Building Extraction · Spatial Cognition and Navigation
