NeSy-Route: A Neuro-Symbolic Benchmark for Constrained Route Planning in Remote Sensing
arXiv:2603.16307v1 Announce Type: new 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 NeSy-Route to comprehensively evaluate planning acros
arXiv:2603.16307v1 Announce Type: new 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 NeSy-Route to comprehensively evaluate planning across 10,821 route-planning samples, nearly 10 times larger than the largest prior benchmark. Furthermore, a three-level hierarchical neuro-symbolic evaluation protocol is developed to enable accurate assessment and support fine-grained analysis on perception, reasoning, and planning simultaneously. Our comprehensive evaluation of various state-of-the-art MLLMs demonstrates that existing MLLMs show significant deficiencies in perception and planning capabilities. We hope NeSy-Route can support further research and development of more powerful MLLMs for remote sensing.
Executive Summary
NeSy-Route is a groundbreaking neuro-symbolic benchmark for constrained route planning in remote sensing, addressing the limitations of existing benchmarks by incorporating a large-scale data-generation framework and a hierarchical evaluation protocol. The benchmark evaluates the planning capabilities of multimodal large language models (MLLMs) across 10,821 route-planning samples, demonstrating significant deficiencies in perception and planning capabilities. NeSy-Route has the potential to support the development of more powerful MLLMs for remote sensing applications. The findings of this study have far-reaching implications for disaster relief, ecological field surveys, and other critical applications relying on remote sensing.
Key Points
- ▸ NeSy-Route is a large-scale neuro-symbolic benchmark for constrained route planning in remote sensing.
- ▸ The benchmark incorporates an automated data-generation framework and a hierarchical evaluation protocol.
- ▸ NeSy-Route evaluates the planning capabilities of MLLMs across 10,821 route-planning samples.
Merits
Strength in Design
NeSy-Route's automated data-generation framework allows for the creation of diverse route-planning tasks with provably optimal solutions, making it an authoritative benchmark for evaluating planning capabilities.
Comprehensive Evaluation
The benchmark's hierarchical evaluation protocol enables accurate assessment and fine-grained analysis of perception, reasoning, and planning simultaneously, providing a comprehensive understanding of MLLMs' capabilities.
Demerits
Limited Generalizability
The study's findings may not generalize to other remote sensing applications or domains, as the benchmark is specifically designed for constrained route planning in remote sensing.
Dependence on Data Quality
The accuracy and adequacy of NeSy-Route's evaluation depend on the quality of the data generated by the automated framework, which may introduce biases or limitations.
Expert Commentary
NeSy-Route is a groundbreaking benchmark that addresses the limitations of existing benchmarks in remote sensing. The study's findings demonstrate significant deficiencies in the planning capabilities of MLLMs, highlighting the need for more robust and powerful models. The benchmark's hierarchical evaluation protocol and automated data-generation framework make it an authoritative tool for evaluating planning capabilities. However, the study's limitations, such as limited generalizability and dependence on data quality, must be considered when interpreting the results. Overall, NeSy-Route has the potential to revolutionize the field of remote sensing by enabling the development of more effective and accurate planning and decision-making systems.
Recommendations
- ✓ Future studies should investigate the generalizability of NeSy-Route to other remote sensing applications and domains.
- ✓ Researchers should develop more robust and powerful MLLMs that can effectively integrate perception, reasoning, and planning capabilities.