annotations_creators: []
language: en
size_categories:

• 10K<n<100K
  task_categories: []
  task_ids: []
  pretty_name: maptrace_20k
  tags:

• fiftyone

• image
  dataset_summary: '

  This is a FiftyOne dataset with 20000 samples.

  Installation

  If you haven''t already, install FiftyOne:

  
  pip install -U fiftyone
  

  Usage

  
  import fiftyone as fo
  
  from fiftyone.utils.huggingface import load_from_hub
  Load the dataset
  Note: other available arguments include ''max_samples'', etc
  dataset = load_from_hub("Voxel51/maptrace_20k")
  Launch the App
  session = fo.launch_app(dataset)
  

  '

Dataset Card for MapTrace-20k

This is a FiftyOne dataset with 20000 samples.

Installation

If you haven't already, install FiftyOne:

pip install -U fiftyone

Usage

import fiftyone as fo
from fiftyone.utils.huggingface import load_from_hub

Load the dataset
Note: other available arguments include 'max_samples', etc
dataset = load_from_hub("Voxel51/maptrace_20k")
Launch the App
session = fo.launch_app(dataset)

Dataset Details

Dataset Description

MapTrace is a synthetic dataset for path tracing on maps. The dataset contains annotated paths designed to train vision-language models on route-tracing tasks. Each sample consists of a map image annotated with start (green) and end (red) positions, along with a natural language prompt and ground truth path coordinates.

The maptrace_20k split used here contains paths on stylized maps such as those found in brochures, park directories, or shopping malls.

• Curated by: Google
• Language(s) (NLP): English
• License: CC-BY-4.0

Dataset Sources

• Repository: https://huggingface.co/datasets/google/MapTrace

Uses

Direct Use

This dataset is intended for training and evaluating vision-language models on spatial reasoning and path-tracing tasks. Models are expected to interpret map images with marked start/end locations and output coordinate sequences representing valid paths between those points.

Dataset Structure

Original Schema (Hugging Face)

The maptrace_20k split contains the following fields:

• image: The image bytes of the map, annotated with start and end positions
• label: A string representation of a list of (x, y) coordinate tuples defining the target path (normalized between 0 and 1)
• input: A natural language prompt asking the model to find the path

FiftyOne Schema

The FiftyOne dataset converts the original format into the following structure:

Sample Fields:

• filepath: Path to the PNG image file
• input (StringField): The natural language prompt describing the task
• ground_truth (Keypoints): The path represented as keypoints with the following properties:
  • Each keypoint is labeled alphabetically (A, B, C, ..., Z, AA, AB, etc.)
  • Points are normalized coordinates in [0, 1] range
  • The number of keypoints varies per sample

Dataset-Level Attributes:

• default_skeleton: A KeypointSkeleton that connects sequential keypoints (A→B→C→D...) to visualize the path as a connected polyline in the FiftyOne App

Dataset Creation

Source Data

Data Collection and Processing

The dataset is synthetically generated. Maps are created using text-to-image generation models from natural language map descriptions. Paths are then annotated on these synthetic map images with start positions marked in green and end positions marked in red.

Citation

BibTeX:

@dataset{maptrace2024,
  title={MapTrace: A 2M-Sample Synthetic Dataset for Path Tracing on Maps},
  author={Google},
  year={2024},
  publisher={Hugging Face},
  url={https://huggingface.co/datasets/google/MapTrace}
}