---
license: other
license_name: openearthmap-derived-mixed-source-dataset-license
license_link: LICENSE
pretty_name: NeSy-Route
task_categories:
- image-to-text
- visual-question-answering
language:
- en
tags:
- remote-sensing
- route-planning
- benchmark
- neural-symbolic
- multimodal-large-language-models
configs:
- config_name: task2
  data_files:
  - split: easy
    path: Task2/parquet/easy/*.parquet
  - split: medium
    path: Task2/parquet/medium/*.parquet
  - split: hard
    path: Task2/parquet/hard/*.parquet
- config_name: task3
  data_files:
  - split: easy
    path: Task3/parquet/easy/*.parquet
  - split: medium
    path: Task3/parquet/medium/*.parquet
  - split: hard
    path: Task3/parquet/hard/*.parquet
- config_name: task2-evaluation
  data_files:
  - split: easy
    path: Task2/evaluation/easy.parquet
  - split: medium
    path: Task2/evaluation/medium.parquet
  - split: hard
    path: Task2/evaluation/hard.parquet
- config_name: task3-evaluation
  data_files:
  - split: easy
    path: Task3/evaluation/easy.parquet
  - split: medium
    path: Task3/evaluation/medium.parquet
  - split: hard
    path: Task3/evaluation/hard.parquet
- config_name: task3-labels
  data_files:
  - split: train
    path: Task3/labels/labels.parquet
---

<div align="center">

<h1>NeSy-Route: A Neuro-Symbolic Benchmark for Constrained Route Planning in Remote Sensing</h1>

<p>
  <a href="https://mingyang1010.github.io/NeSy-Route/">
    <img src="https://img.shields.io/badge/Project-Page-34A853?style=flat-square&logo=googlechrome&logoColor=white" alt="Project Page">
  </a>
  &nbsp;&nbsp;
  <a href="https://arxiv.org/abs/2603.16307">
    <img src="https://img.shields.io/badge/Paper-arXiv-B31B1B?style=flat-square&logo=arxiv&logoColor=white" alt="Paper">
  </a>
  &nbsp;&nbsp;
  <a href="https://github.com/MingYang1010/NeSy-Route">
    <img src="https://img.shields.io/badge/Code-GitHub-0969DA?style=flat-square&logo=github&logoColor=white" alt="Code">
  </a>
</p>

</div>

## Dataset Composition

NeSy-Route is organized into three benchmark tasks for evaluating perception, symbolic reasoning, and constrained route planning over remote-sensing imagery.

| Task | Setting | Easy | Medium | Hard | Total | Content |
| --- | --- | ---: | ---: | ---: | ---: | --- |
| Task 1 | Few-shot | - | - | - | 3,607 | Semantic traversability and cost-vector prediction |
| Task 2 | Zero-shot | 7,659 | 3,712 | 1,604 | 12,975 | Constraint-aware semantic and region reasoning |
| Task 3 | Zero-shot | 6,492 | 2,705 | 1,624 | 10,821 | Constrained route planning with waypoint or trajectory outputs |

Task 1 and Task 2 provide semantic constraint annotations for vector-based reasoning. Task 3 provides route-planning samples with endpoint annotations, optimal ground-truth trajectories, trajectory visualizations, and difficulty-level subsets.

Task 2 annotated images are packaged as Parquet shards under `Task2/parquet/{easy,medium,hard}/`. Each row includes the annotated image bytes and the corresponding query, region, and difficulty metadata.

Task 3 samples are packaged as Parquet shards under `Task3/parquet/{easy,medium,hard}/`. Each row includes the original remote-sensing image, query and endpoint metadata, the optimal ground-truth trajectory, and its visualization.

Lightweight scoring annotations are available under `Task2/evaluation/` and `Task3/evaluation/`. Task 3 also provides 3,109 deduplicated semantic masks in a single `Task3/labels/labels.parquet` file. The evaluation code combines each mask with the sample-specific traversability and cost vectors to reconstruct the maps used by AR, CR, and VR.

## Loading the Data

```python
from datasets import load_dataset

task2 = load_dataset("Ming1010/NeSy-Route", "task2")
task3 = load_dataset("Ming1010/NeSy-Route", "task3")

# Lightweight ground truth used by the public scorer
task2_eval = load_dataset("Ming1010/NeSy-Route", "task2-evaluation")
task3_eval = load_dataset("Ming1010/NeSy-Route", "task3-evaluation")
task3_labels = load_dataset("Ming1010/NeSy-Route", "task3-labels", split="train")
```

## Evaluation Code

Evaluation scripts, prompt templates, and running instructions are available in the GitHub repository:

<https://github.com/MingYang1010/NeSy-Route>

## License

NeSy-Route follows the same licensing policy as OpenEarthMap. Label data are released under the same license as the corresponding source RGB imagery when applicable. For data whose source imagery is public domain or whose license is otherwise unspecified, the released labels follow the **Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0)**.

Please also check the `LICENSE` file in this repository before using or redistributing the dataset.