---
language:
- en
license: mit
task_categories:
- text-generation
pretty_name: "TurnBack 10% Stratified Sample"
tags:
- geospatial
- route-reversal
- route-planning
- navigation
- spatial-reasoning
- large-language-models
- benchmark
- stratified-sample
source_datasets:
- bghjmn32/EMNLP2025_Turnback
size_categories:
- 1K<n<10K
---

# 🧭 TurnBack 10% Stratified Sample

### A deterministic route-reversal subset for lightweight geospatial cognition experiments

<p align="center" style="text-align:center; white-space:nowrap;"><a href="https://huggingface.co/datasets/zhangdw/TurnBack-10pct"><img src="https://img.shields.io/badge/Hugging%20Face-Dataset-yellow?logo=huggingface" alt="Hugging Face Dataset" style="display:inline-block; vertical-align:middle; margin:0 3px;"></a>&nbsp;<img src="https://img.shields.io/badge/License-MIT-blue" alt="License MIT" style="display:inline-block; vertical-align:middle; margin:0 3px;">&nbsp;<img src="https://img.shields.io/badge/Routes-3%2C900-00b894" alt="3,900 routes" style="display:inline-block; vertical-align:middle; margin:0 3px;">&nbsp;<img src="https://img.shields.io/badge/Cities-13-6c5ce7" alt="13 cities" style="display:inline-block; vertical-align:middle; margin:0 3px;">&nbsp;<img src="https://img.shields.io/badge/Strata-City%20%C3%97%20Difficulty-fd79a8" alt="city by difficulty strata" style="display:inline-block; vertical-align:middle; margin:0 3px;"></p>

<p align="center">
  <b>TurnBack 10%</b> is a balanced, reproducible subset of the public TurnBack route corpus for route-level reverse-navigation generation and evaluation.
</p>

<p align="center">
  <a href="#-quick-start"><b>Quick Start</b></a> ·
  <a href="#-dataset-at-a-glance"><b>At a Glance</b></a> ·
  <a href="#-files"><b>Files</b></a> ·
  <a href="#-sampling-policy"><b>Sampling</b></a> ·
  <a href="#-citation"><b>Citation</b></a>
</p>

---

> [!IMPORTANT]
> This is a **10% stratified sample** of the public raw route corpus from `bghjmn32/EMNLP2025_Turnback`, not the full TurnBack benchmark. It is intended for lightweight route-reversal experiments, prompt iteration, and reproducibility checks before full-scale evaluation.

## ✨ Why This Dataset?

TurnBack evaluates route-level geospatial cognition: a model receives forward pedestrian navigation instructions and must generate reverse instructions that guide the pedestrian back to the original start. The reference geometry is the original route traversed backwards, and downstream evaluation should execute the generated reverse instructions with Path Builder before scoring recovered geometry.

This subset is designed for questions like:

- **Can a model reverse pedestrian navigation instructions across cities and difficulty levels?**
- **How does route-reversal quality change from easy to hard routes?**
- **Can a geospatial evaluator run quickly on a balanced sample before full TurnBack?**
- **Can every sampled route be traced back to official raw instructions and route geometry?**

## 📦 Dataset at a Glance

<table>
  <tr>
    <td align="center"><b>3,900</b><br/>sampled routes</td>
    <td align="center"><b>13</b><br/>public city directories</td>
    <td align="center"><b>3</b><br/>difficulty levels</td>
    <td align="center"><b>100</b><br/>routes per stratum</td>
  </tr>
  <tr>
    <td align="center"><b>1,300</b><br/>easy routes</td>
    <td align="center"><b>1,300</b><br/>medium routes</td>
    <td align="center"><b>1,300</b><br/>hard routes</td>
    <td align="center"><b>20260606</b><br/>sampling seed</td>
  </tr>
</table>

### Covered cities

`Auckland_New_Zealand` · `Cairo_Egypt` · `Cape_Town_South_Africa` · `Denver_Colorado_USA` · `London_UK` · `Mexico_City_Mexico` · `Munich_Germany` · `Paris_France` · `Rio_de_Janeiro_Brazil` · `Singapore_Singapore` · `Sydney_Australia` · `Tokyo_23_wards` · `Toronto_Canada`

### Evaluation shape

| Component | Description |
|---|---|
| Input | Forward pedestrian navigation instructions from the official raw corpus. |
| Output | Model-generated reverse instructions back to the original start. |
| Reference | Original route geometry traversed backwards. |
| Recommended scorer | Execute generated instructions with Path Builder, then compare recovered route geometry. |
| Interaction pattern | One prompt / response per sampled route. |

## 🚀 Quick Start

Load the JSONL dataset:

```python
from datasets import load_dataset

ds = load_dataset(
    "json",
    data_files="https://huggingface.co/datasets/zhangdw/TurnBack-10pct/resolve/main/data/turnback_10pct.jsonl",
    split="train",
)

print(len(ds))
print(ds[0].keys())
```

Inspect one route-reversal prompt:

```python
row = ds[0]
print(row["city"], row["difficulty"], row["route_id"])
print(row["instructions_text"][:1000])
print(row["prompt"][:1000])
```

Download the full subset, metadata, and regeneration script:

```bash
hf download zhangdw/TurnBack-10pct \
  --type dataset \
  --local-dir data/turnback-10pct
```

## 🗂️ Files

| Path | Description |
|---|---|
| `data/turnback_10pct.jsonl` | One JSON object per sampled route. |
| `metadata/counts.json` | Sampling settings, city/difficulty counts, and validation summary. |
| `metadata/sample_ids.json` | Sampled route IDs for every city × difficulty cell. |
| `scripts/build_turnback_10pct_dataset.py` | Exact script used to regenerate this dataset from the official source. |

Each JSONL record contains:

| Field | Description |
|---|---|
| `id`, `city`, `difficulty`, `route_id` | Stable route identifiers and strata. |
| `instructions_text` | Forward navigation instructions from the official raw corpus. |
| `route_geojson` | Reference route geometry from the official raw corpus. |
| `route_summary` | Distance, duration, step count, coordinate count, bbox, and start/end coordinates. |
| `prompt` | A simple route-reversal prompt built from the forward instructions. |
| `source_paths`, `source_urls` | Provenance back to the official GitHub raw files. |

## 🧭 Example Workflows

<details open>
<summary><b>Count routes by city and difficulty</b></summary>

```python
import pandas as pd

df = ds.to_pandas()
print(df.groupby(["city", "difficulty"]).size())
print(df.groupby("difficulty").size())
```

</details>

<details>
<summary><b>Read the sampling manifest</b></summary>

```python
import json
from huggingface_hub import hf_hub_download

path = hf_hub_download(
    repo_id="zhangdw/TurnBack-10pct",
    repo_type="dataset",
    filename="metadata/counts.json",
)

with open(path, "r", encoding="utf-8") as f:
    counts = json.load(f)

print(counts["total_sampled_routes"])
print(counts["counts_by_difficulty"])
```

</details>

<details>
<summary><b>Build a simple model input</b></summary>

```python
messages = [
    {
        "role": "system",
        "content": "You reverse pedestrian navigation instructions while preserving route geometry.",
    },
    {"role": "user", "content": row["prompt"]},
]
```

</details>

## 🧪 Sampling Policy

| Setting | Value |
|---|---|
| Source repository | `bghjmn32/EMNLP2025_Turnback` |
| Source revision | `main` |
| Source root | `36kroutes/` |
| Sampling seed | `20260606` |
| Sampling fraction | `0.1` |
| Stratification | `city × difficulty` |
| Sample per cell | 100 routes |
| Route ID universe per cell | `0..999` |
| Total sampled routes | 3,900 |
| Created at | `2026-06-06T18:31:38.193396+00:00` |

The original paper describes a 36,000-route benchmark over 12 metropolitan areas. The currently published raw `36kroutes/` snapshot in the official GitHub repository contains an additional public city directory, `Paris_France`. The official public code can enumerate, load, and score that city, so this subset includes all 13 public city directories. To preserve a paper-style balanced layout, it samples from the canonical route-id universe `0..999` in each city × difficulty cell and excludes overflow route directories that appear in some raw-snapshot cells.

## 📊 Counts by Stratum

Every city contributes 300 routes: 100 easy, 100 medium, and 100 hard.

<details>
<summary><b>Full city × difficulty table</b></summary>

| City | Easy | Medium | Hard | Total |
|---|---:|---:|---:|---:|
| Auckland_New_Zealand | 100 | 100 | 100 | 300 |
| Cairo_Egypt | 100 | 100 | 100 | 300 |
| Cape_Town_South_Africa | 100 | 100 | 100 | 300 |
| Denver_Colorado_USA | 100 | 100 | 100 | 300 |
| London_UK | 100 | 100 | 100 | 300 |
| Mexico_City_Mexico | 100 | 100 | 100 | 300 |
| Munich_Germany | 100 | 100 | 100 | 300 |
| Paris_France | 100 | 100 | 100 | 300 |
| Rio_de_Janeiro_Brazil | 100 | 100 | 100 | 300 |
| Singapore_Singapore | 100 | 100 | 100 | 300 |
| Sydney_Australia | 100 | 100 | 100 | 300 |
| Tokyo_23_wards | 100 | 100 | 100 | 300 |
| Toronto_Canada | 100 | 100 | 100 | 300 |
| **Total** | **1,300** | **1,300** | **1,300** | **3,900** |

</details>

## ✅ Intended Use

This subset is useful for:

- quick route-reversal prompt and evaluator iteration;
- balanced city × difficulty smoke tests;
- geospatial instruction-following experiments;
- provenance-preserving route-level error analysis;
- validating code before running the full TurnBack corpus.

It is not a replacement for full TurnBack benchmark reporting.

## ⚖️ License

This subset follows the MIT license metadata of the upstream public TurnBack release. Check the official TurnBack repository and paper for source-specific requirements.

## 📚 Citation

If you use this dataset, cite the original TurnBack paper and official code/data release:

```bibtex
@inproceedings{luo2025turnback,
  title = {TurnBack: A Geospatial Route Cognition Benchmark for Large Language Models through Reverse Route},
  author = {Luo, Hongyi and Cheng, Qing and Matos, Daniel and Gadi, Hari Krishna and Zhang, Yanfeng and Liu, Lu and Wang, Yongliang and Zeller, Niclas and Cremers, Daniel and Meng, Liqiu},
  booktitle = {Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing},
  year = {2025}
}
```

You may also cite this deterministic subset when the 10% sampling policy matters for reproducibility:

```bibtex
@misc{turnback_10pct2026,
  title        = {TurnBack 10% Stratified Sample},
  author       = {Dawei Zhang},
  year         = {2026},
  howpublished = {Hugging Face Dataset},
  url          = {https://huggingface.co/datasets/zhangdw/TurnBack-10pct}
}
```

---

<div align="center">

<b>A compact route-reversal sample for balanced geospatial cognition experiments.</b>

</div>