Blueprint Hub

Map Features in OpenStreetMap with Computer Vision is a Mozilla.ai Lumigator Blueprint that demonstrates how to fine-tune a computer vision object-detection model to map features in OpenStreetMap (OSM), with an added layer of human verification. It serves as a guided starting point for building open-source AI workflows that transform visual data into structured map features, while enabling collaboration and validation by humans to ensure accuracy in mapping tasks.

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Overview

• Goal: Train an object-detection model capable of identifying map-relevant features (e.g., roads, buildings, barriers, water bodies) in imagery and align them with OpenStreetMap feature types.
• Accessibility: Designed for developers and researchers who want to create tailored computer vision pipelines for OSM feature mapping.
• Human-in-the-loop: Incorporates a verification step where humans review and correct model outputs to maintain high data quality for map data.
• Open-source emphasis: Built within Mozilla.ai’s blueprint framework to encourage open collaboration and reuse.

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How to Use Map Features in OpenStreetMap with Computer Vision

1. Review prerequisites. Ensure you have a labeled dataset of imagery with corresponding OpenStreetMap feature labels (e.g., road, building, water, vegetation). Access to a compute environment capable of training object detection models (e.g., PyTorch, through the blueprint workflow).
2. Set up the blueprint. Import the blueprint into your Lumigator/Open-Source AI workflow environment. Configure dataset paths, label mappings to OSM feature types, and any domain-specific preprocessing steps.
3. Train the model. Run the object-detection training process. The blueprint guides you through configuring model architecture (e.g., RetinaNet, YOLO variants, or custom detectors) and training hyperparameters.
4. Evaluate and iterate. Assess model performance on a validation set, focusing on precision/recall for mapped OSM features. Iterate on data labeling, augmentation, and model choice to improve results.
5. Apply to imagery and map features. Use the trained model to infer features on new imagery (e.g., satellite or aerial photos) and generate a structured output aligned with OpenStreetMap schema.
6. Human verification. Route model outputs to human validators to confirm or correct detected features, ensuring alignment with OSM tagging conventions and local context.
7. Publish and sync. Integrate verified features into an OSM-ready workflow, enabling contributions back to the map data and collaboration with the OSM community.

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Key Concepts

• Object detection for map feature extraction: Detects visual features in imagery and assigns them to predefined OSM feature categories.
• Feature mapping to OSM: Aligns detected classes with OpenStreetMap tags and geometry (points, lines, polygons) suitable for map data.
• Human verification: A quality-control layer where humans review detections to fix misclassifications and refine boundaries.
• Open-source and reproducible: Blueprint format encourages reuse, modification, and sharing of workflows.

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Outputs

• Trained object-detection model weights and configurations.
• Predictions on new imagery with associated OSM feature labels.
• Annotated datasets and evaluation metrics suitable for improving mapping pipelines.
• Documentation and templates to integrate with existing OSM import/export workflows.

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Benefits and Use Cases

• Accelerates feature mapping in OpenStreetMap by leveraging automated detection.
• Enables customized mapping workflows for specific locales or feature priorities.
• Supports community-driven improvement of map data with human-in-the-loop validation.

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Safety and Considerations

• Model predictions should be reviewed by humans before contributing to OpenStreetMap to avoid incorrect tagging.
• Be mindful of licensing and data sources when using imagery for mapping projects.

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Core Features

• Open-source blueprint designed for fine-tuning an object-detection model targeting OpenStreetMap feature mapping
• Human verification step to ensure accuracy and quality of map data
• End-to-end workflow from data preparation to model deployment and mapping integration
• Flexible model choices and dataset customization to suit different locales and feature types
• Reusable blueprint format within Mozilla.ai Lumigator for collaborative development
• Clear guidance for training, evaluation, and integration with OSM tagging schemas