Reasonmap: Fine-Grained Visual Reasoning from Transit Maps

Reasonmap is a benchmark dataset for evaluating multimodal large language models on fine-grained visual reasoning tasks using transit maps. The dataset was created by FSCCS and is associated with a research paper and project page. It was last updated on January 6,我们发现了一个问题。

Use Cases

• Benchmarking MLLM performance on visual question answering based on transit map imagery.
• Training models for fine-grained spatial reasoning based on map layouts and station relationships.
• Developing AI assistants for navigation and route planning based on visual transit information.

Strengths

• Dataset is directly linked to a published research paper, providing academic context.
• Associated with a dedicated project page and GitHub repository for code and data access.
• Last updated on 2026-01-06, indicating recent maintenance.

Limitations

• Row count, file size, and specific file formats are unknown, limiting suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• License information is not provided, which may restrict usage.

Provenance

Source

FSCCS, via the associated research paper and GitHub repository.

Collection Method

Likely created for the benchmark study described in the paper 'Can MLLMs Guide Me Home? A Benchmark Study on Fine-Grained Visual Reasoning from Transit Maps'.

Time Range

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Freshness

Last updated 2026-01-06 06:08:00

Geography

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