https://github.com/dnerini/startleiter

A data-driven paragliding buddy.
https://github.com/dnerini/startleiter

machine-learning paragliding radiosonde

Last synced: 3 months ago
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A data-driven paragliding buddy.

• Host: GitHub
• URL: https://github.com/dnerini/startleiter
• Owner: dnerini
• License: bsd-3-clause
• Created: 2021-01-10T17:07:42.000Z (almost 5 years ago)
• Default Branch: main
• Last Pushed: 2025-07-15T05:21:11.000Z (3 months ago)
• Last Synced: 2025-07-15T11:51:32.308Z (3 months ago)
• Topics: machine-learning, paragliding, radiosonde
• Language: Python
• Homepage: https://dnerini.github.io/startleiter/
• Size: 1.82 GB
• Stars: 12
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

README

          
Welcome to Starleiter! In this project, I used data analysis and machine learning techniques

to explore the relationship between the atmospheric conditions and paragliding.

## Project Overview

Startleiter is a recommendation system for paragliding pilots. Based on the nearest and

most recently available radio-sounding, it computes the probability of flying on the

current day, as well as the expected maximum flying height and distance.

The prediction model, a one-dimensional convolutional neural network (1D CNN),

is trained on radio-sounding data from [UWYO](http://weather.uwyo.edu/upperair/sounding.html)

and flight reports from [XContest](https://www.xcontest.org/world/en/). 

Startleiter also includes an explainability plot based on [SHAP](https://github.com/slundberg/shap)

to gain insights on the output of the machine learning model, for example:

![](https://user-images.githubusercontent.com/11967971/178354681-50b8b017-b007-4dd0-99e9-1c5f30e789cb.png)

## Project Components

The project consists of the following components:

- Data extraction.

- [Data exploration and visualization](https://dnerini.github.io/startleiter/statistics.html).

- Data preprocessing and feature engineering.

- Model training and evaluation.

- [Predictions](https://dnerini.github.io/startleiter/monitoring.html).

## Credits and Sources

- Flight reports: [XContest](https://www.xcontest.org/)

- Atmospheric soundings: [University of Wyoming](https://weather.uwyo.edu/upperair/sounding.html)

- GFS forecast data: [NOAA](https://rucsoundings.noaa.gov/)

- Explainability score: [SHAP](https://github.com/slundberg/shap)

- SkewT plot: [MetPy](https://unidata.github.io/MetPy/latest/)