GPX Surface Analyzer

Purpose

The GPX Surface Analyzer is a Python-based web application designed to analyze GPX tracks for their suitability for different types of biking, specifically road biking and gravel biking. It calculates:

• Surface Types: Breakdown of road surfaces along the route.
• Suitability Scores: Scores for road biking and gravel biking based on surface types.
• Elevation Data: Total elevation gain and loss along the route.

This tool is ideal for cyclists planning trips who need insights into route surfaces and elevation changes.

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Setup Instructions

Prerequisites

• Python 3.9 or newer
• Docker (if running the application in a container)
• Task (https://taskfile.dev/)

Local Installation

1. Clone the repository:

   git clone https://github.com/kotaicode/gpx_analyzer
   cd gpx_analyzer

2. Install dependencies:

   pip install -r requirements.txt

3. Run the Flask application:

   python app.py

4. The application will be available at http://127.0.0.1:5000.

Using Docker

1. Build the Docker image:

   docker build -t gpx-surface-analyzer .

2. Run the Docker container:

   docker run -p 5000:5000 gpx-surface-analyzer

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Usage

1. Send a POST request to the /analyze_surface endpoint with a GPX file. Example using curl:

   curl -X POST -F "gpx_file=@examples/069-gravelbike-track.gpx" http://127.0.0.1:5000/analyze_surface

2. The response will include:

   • Surface Lengths: A breakdown of surface types and their respective lengths (in km).
   • Suitability Scores: Scores for road and gravel biking.
   • Elevation Data: Total elevation gain and loss (in meters).

Example Response:

{
  "surface_lengths_km": {
    "asphalt": 5.2,
    "gravel": 3.7,
    "unknown": 1.1
  },
  "suitability_scores": {
    "roadbike": 0.8,
    "gravelbike": 1.0
  },
  "elevation_data": {
    "elevation_up": 120.5,
    "elevation_down": 115.3
  }
}

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Next Steps and Possible Improvements

1. Extended Surface Support:

   • Add more granular mapping for surface types.
   • Improve handling of unknown surfaces by inferring based on context.

2. Performance Optimization:

   • Cache Overpass API results for frequently queried areas.
   • Optimize GPX parsing for large files.

3. Enhanced Suitability Metrics:

   • Incorporate other factors like slope and weather conditions.

4. Frontend Integration:

   • Build a web interface for uploading GPX files and visualizing results.

5. Custom OSM Support:

   • Enable querying custom OpenStreetMap instances with user-provided data.

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License

This project is licensed under the MIT License.