--- title: MapTrace canonical_url: "https://www.modelscope.cn/datasets/google/MapTrace" md_url: "https://www.modelscope.cn/datasets/google/MapTrace.md" repository: google/MapTrace last_updated: 2026-06-27 license: cc-by-4.0 storage_size: "215 GB" downloads: 256 stars: 2 --- # MapTrace > MapTrace - google 在 ModelScope 开源的数据集。MapTrace: A 2M-Sample Synthetic Dataset for Path Tracing on Maps google/MapTrace 是 ModelScope 魔搭社区上的数据集，存储大小 215 GB，采用 cc-by-4.0 许可。 - **Repository**: google/MapTrace - **License**: cc-by-4.0 - **Storage size**: 215 GB - **Downloads**: 256 - **Stars**: 2 - **Last updated**: 2026-06-27 Source: https://www.modelscope.cn/datasets/google/MapTrace --- # MapTrace: A 2M-Sample Synthetic Dataset for Path Tracing on Maps

Welcome to the **MapTrace** dataset! If you use this dataset in your work, please **[cite our paper below](#citation)**. For more details about our methodology and findings, please visit our [project page](https://artemisp.github.io/maptrace/) or read the official [white paper](https://arxiv.org/abs/2512.19609). This work was also recently featured on the [Google Research Blog](https://research.google/blog/teaching-ai-to-read-a-map/). ## Code & Scripts Official training and data loading scripts are available in our GitHub repository: **[google-research/MapTrace](https://github.com/google-research/MapTrace)** ## Quick Start: Downloading the Dataset To easily download and work with MapTrace locally, we recommend using the Hugging Face `datasets` library for browsing. But you can also download the raw files directly to train on the entire set. ### Method 1: Using huggingface_hub library to download all raw files. #### 1. Install library First, install the `datasets` and `matplotlib` package in your environment: ```bash pip install huggingface_hub ``` #### 2. Download with python Now download with python ```python from huggingface_hub import snapshot_download local_dir = "./MapTrace_Data" snapshot_download( repo_id="google/MapTrace", repo_type="dataset", local_dir=local_dir ) print(f"All raw parquet files downloaded to: {local_dir}") ``` Note: You can also specify `allow_patterns="maptrace_20k/*"` to download a specific split. ### Method 2: Using datasets library to browse through the data. #### 1. Install the library First, install the `datasets` and `matplotlib` package in your environment: ```bash pip install datasets pip install matplotlib ``` #### 2. Load specific splits Because the dataset is organized into separate folders of `.parquet` files, you can use the `data_dir` argument to load the specific subset you want: ```python from datasets import load_dataset # Load the 20k subset used in the paper ds_20k = load_dataset("google/MapTrace", data_dir="maptrace_20k") # Load the floor maps ds_floormaps = load_dataset("google/MapTrace", data_dir="floormaps") # Load the large complex maps dataset ds_maptrace = load_dataset("google/MapTrace", data_dir="maptrace") ``` #### 3. Browsing the data ```python import io import ast import matplotlib.pyplot as plt from PIL import Image from datasets import load_dataset # 1. Load the dataset folder print("Loading dataset...") # maptrace split ds = load_dataset("google/MapTrace", data_dir="maptrace_20k") # Print the automatically generated splits (e.g., dict_keys(['train', 'validation'])) print(f"Available splits: {ds.keys()}") # Access the first sample from your preferred split (e.g., 'validation' or 'train') split_name = "train" # Change this to "validation" if you prefer sample = ds[split_name][0] # 2. Decode the raw image bytes into a PIL Image img_bytes = sample["image"] img = Image.open(io.BytesIO(img_bytes)).convert("RGB") width, height = img.size # 3. Parse the label text into a list of coordinates normalized_coords = ast.literal_eval(sample["label_"]) # 4. Scale the normalized [0, 1] coordinates to the actual image pixel dimensions pixel_coords = [(x * width, y * height) for x, y in normalized_coords] # 5. Print the text fields print("\n--- Map Information ---") print(f"Input Prompt: {sample['input']}") # 6. Plot the image and the path plt.figure(figsize=(10, 10)) plt.imshow(img) # Unzip the coordinates into separate x and y lists for plotting x_coords, y_coords = zip(*pixel_coords) # Plot the path line and overlay points plt.plot(x_coords, y_coords, color='red', linewidth=3, label='Path') plt.scatter(x_coords, y_coords, color='blue', s=40, zorder=5, label='Waypoints') # Mark the Start and End points clearly plt.scatter(x_coords[0], y_coords[0], color='green', s=100, marker='*', zorder=6, label='Start') plt.scatter(x_coords[-1], y_coords[-1], color='orange', s=100, marker='X', zorder=6, label='End') plt.title(f"MapTrace Path Visualization ({split_name.capitalize()} Split)") plt.axis('off') # Hide axes for a cleaner look plt.legend() # 7. Save the plot instead of showing it to avoid the FigureCanvasAgg warning output_filename = f"visualized_path_{split_name}.png" plt.savefig(output_filename, bbox_inches='tight', dpi=300) print(f"Success! Map visualization saved locally to: {output_filename}") ``` ## Dataset Format This dataset contains 2 million annotated paths designed to train models on route-tracing tasks. ### Data Splits The dataset contains 2M annotated paths designed to train models on route-tracing tasks. Splits: - `maptrace_parquet`: Contains paths on more complex, stylized maps such as those found in brochures, park directories or shopping malls. - `floormap_parquet`: Contains paths on simpler, structured floor maps, typical of office buildings appartment complexes, or campus maps. - `maptrace_20k`: Contains paths on more complex, stylized maps such as those found in brochures, park directories or shopping malls and this subset was used for our paper `MapTrace: Scalable Data Generation for Route Tracing on Maps`. ### Schemas Splits `maptrace_parquet` and `floormap_parquet` has the following fields: - `image_bytes`: The raw bytes of the generated map image (without post processing.) - `label_text`: A string representation of a list of coordinates defining the target path. All coordinates are normalized between 0 and 1. - `input_text`: A natural language question (prompt) asking the model to find the path specified in `label_text`. - `map_description`: A natural language description of the map image, used by a text-to-image generation model to create the synthetic image. We also release the splits used in our paper in `maptrace_20k`. The schema in these files is as follows: - `image`: The image bytes of the map, *annotated* with start and end positions - `label`: A string representation of a list of coordinates defining the target path. All coordinates are normalized between 0 and 1. - `input`: A natural language question (prompt) asking the model to find the path specified in `label`. ## Citation If you use our work, please cite: ```bibtex @misc{panagopoulou2025maptracescalabledatageneration, title={MapTrace: Scalable Data Generation for Route Tracing on Maps}, author={Artemis Panagopoulou and Aveek Purohit and Achin Kulshrestha and Soroosh Yazdani and Mohit Goyal}, year={2025}, eprint={2512.19609}, archivePrefix={arXiv}, primaryClass={cs.CV}, url={https://arxiv.org/abs/2512.19609}, } ```