TerraHex

**TerraHex** analyzes GPS data, applies smoothing techniques, and visualizes paths via 2D, 3D, and animated plots. It identifies navigation errors and maps routes using real-world survey data

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G-Hacks 2025: GPS to Remove Stress

Overview

This project, TerraHex, was developed as part of the G-Hacks 2025 Hackathon by Team G5 (Asim Hamdan, Neeor Alam, and Constantine Saev) on February 22-23, 2025. The project focuses on GPS data analysis and smoothing techniques to better understand real-world navigation errors and improve positioning accuracy.

Project Goals

  • Analyze GPS data collected from surveying.
  • Apply various smoothing methods (Gaussian, EMA, Kalman Filtering) to reduce errors in altitude readings.
  • Visualize GPS data through 2D, 3D, and animated plots.
  • Identify sources of error in GPS readings and their impact on real-world tracking.

Key Sections of the Website

  1. Introduction

    • Explains the importance of navigation and GPS tracking.
    • Discusses how sensors collect and process location data.

  2. Graphs and Figures

    • Displays 2D and 3D plots of the surveyed walking path.
    • Includes an animated route visualization.
    • Features a smoothed version of the path to correct noise.

  3. Sources of Error

    • Discusses GPS inaccuracies due to buildings, trees, and terrain.
    • Highlights how data smoothing reduces, but doesn’t eliminate errors.

  4. Conclusion

    • Reflects on the importance of geomatics in modern navigation.
    • Discusses lessons learned from using Hexagon's surveying equipment.
    • Evaluates the effectiveness of Gaussian smoothing in reducing noise.

  5. Logbook

    • A detailed timeline documenting our workflow over February 22-23, 2025.
    • Tracks progress from data collection to website finalization.

Technical Features

  • GPS Data Processing: Extracts, analyzes, and visualizes longitude, latitude, and altitude.
  • Smoothing Methods Implemented:
    • Gaussian Smoothing (Best performing method)
    • Exponential Moving Average (EMA)
    • Kalman Filtering (Advanced predictive smoothing)
  • Visualizations:
    • Matplotlib for static graphs
    • Folium for interactive maps
    • Matplotlib Animation for route playback
  • Web Technologies Used:
    • HTML, CSS for the website
    • Python for the data processing and plotting

How to Use

  1. Navigate through the website to explore graphs, visualizations, and analysis.
  2. View the interactive walking path (smoothed GPS data plotted on a real map).
  3. Check the logbook for insights into the team's development process.
  4. Access the full dataset via the table of averaged data.

Future Improvements

  • Implement celestial body tracking for more fun information
  • Integrate machine learning models to predict and correct GPS errors from buildings and trees.
  • Develop a mobile-friendly version website.

Team Members

  • Asim Hamdan (Data Processing & Algorithm Development)
  • Neeor Alam (Visualization & Web Development)
  • Constantine Saev (Data Collection & Analysis)

Acknowledgments

We sincerely thank Hexagon for providing the surveying equipment and the G-Hacks 2025 organizers for this opportunity to innovate in the field of geomatics and GPS technology.

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