Macunaíma

Campinas, São Paulo | Mapping Space Trash in Real Time

Real Time Debris Tracking

High-Level Project Summary

Our Project is divided in two parts. The first one is a back-end application, responsible for the data stream, collecting online geospatial data, processing it to get the geographical coordinates of the current spatial debris orbiting Earth. The final part consists of an application to track and visualize these debris data.That way, it is possible to locate the known debris and have real time information about them, therefore making it possible to predict possible solutions.

Link to Project "Demo"

https://www.powtoon.com/s/f30i8gYIA55/1/m

Link to Final Project

https://github.com/luamzi/nasa-space-apps-challenge-2021

Detailed Project Description

The front-end part was developed using android and android studio. The first thing we did was set some objectives that we would have to accomplish, which were in their respective order:

Develop an application able to show debris in real time
Be able to filter debris by position or name
Predict when a debris pass near a determinate place
Take the current location and show the closest debris
Show debris positions in different times(past data and future predictions)

The back-end was built with python and it ran within a Jupyter Notebook inside google colab. Within a loop, we used the SpaceTrack Python API to download the Two-line element set of some satellites and Debris. Then we used the SGP4 Python library to use its propagator and, at last, we used the Astropy Python library to convert the data to the Geographic coordinate system, which was automatically updated in a json file in Github. With that, the data.json was available for the Android application.

During the development, NASA’s World Wind library was extremely used. With it, it was possible to render the globe and the data in our app. With this part done and while working in the backend to take the data from the space-tracker api, we started looking through forms of filtering the data and predicting. These aims were not possible to be implemented due to time. So, after making the backend work, we started to work with the requests from the app. To do this, we created an async task in our app to keep requesting data updates. Although a large part of the project was developed during the hackaton, there are still possibilities to implement features that can add a lot such as those mentioned above.

Space Agency Data

First of all, we used the NEOSSAT - Astronomy Data from the Canada Space Agency and Celestrack to get familiarized with the type of data we would work on. Celestrak’s resources were also the first data source we used to test the data stream in order to convert the data to a type that the WorldWind API could use.

Finally, the data really used in the project was obtained from the Space-Track Platform, which provides geospatial data from the currently known debris orbiting Earth. These data were obtained using the Space-Track Python API, and they were all in the TLE format, being converted to geographical coordinates, through True Equator Mean Equinox and SGP4 data formats.

Hackathon Journey

It was awesome to learn more about the availability of open-source data and software that allows even college students like ourselves to build full applications to solve problems about space.

Besides, it was great to learn, through the challenge and at practice, how to build a data stream, finding new tools, knowing new data types, working with NASA's World Wind library and, most of all, develop a totally new project, at least for us, from scratch.

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