Note

This is a Hugging Face dataset. For large datasets, ensure huggingface_hub>=1.1.3 to avoid rate limits. Learn more in the Hugging Face integration docs.

Hugging Face

Dataset Card for MapTrace-20k#

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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}
}