Phoenix United

https://youtu.be/dOoy1fcPCJ0

https://github.com/vishali09/DEB

Project Overview:

We have developed our project for localizing the debris or space junk into their accurate orbits by which we can analyse and visualise also about the real time position of debris and its level nearby to the planets. This platform actually assures us the main topology of the basic principle of debris classification. Because, without debris calcification we are unable to produce the best prediction for securing our earth or other planets as a we know that how much dangerous can be the debris are.

Tools and Methods:

We have built a machine learning software-based application using the rich library-based language python and for visualising with a user-friendly environment we have taken the favour of HTML, Jas and CSS. Machine learning technology we have used only for normalizing data which we have collected from different data source of Nasa and for the classification of debris. We have implemented our own model named support regression machine model which is the best combination of support vector machine and linear regression model. After that we have integrated the general API and features of google API with life map tracking,

Result and Output:

Our output basically shows us the real time tracking output with the basic and equal form of google earth. As google earth API integration key has been generated in this output so can easily visualize and realise the output and the form for mapping and heatmap with the usage of seaborn library. Our total output basically shows the orbits and debris position with quantity so that we can make a prediction into their score or the accuracy of the levels.

We have used mostly Nasa and Leolab data because our main focus and aim is to localize debris with the comparison between their origin and speed. As our motive is also to ttrack the real time pathway of debris thats why we have used the last orbital infirmation, debris location ,axis, epoch dataset from leolab and Nasa open data platform. We have not gotten any kind of difficulties at the time of analysis because both agencies were sorted their data in an well mannerd format with proper validation. We have researched for space lot of time thats why NAsa data source inspiters us to go into this.

Team Phoenix United has always been so much excited for this Year Nasa International Space app challenge, It was a challenging task for all but with hardwork and dedication we were able to complete the challenge on time. The team started working on the project one month advance gathering all the resources arranging meetings and scheduling the tasks. The team enjoyed trying out diffent models to find out the maximum accuracy possible and making the project a great sucess.

1) DigitalGlobe (https://www.digitalglobe.com)

2) JSC Vimpel (http://spacedata.vimpel.ru)

3) LeoLabs (https://www.leolabs.space)

4) Planet (https://www.planet.com)

5) SeeSat-L ( http://www.satobs.org/seesat/seesatindex.html, https://www.prismnet.com/~mmccants/tles)

6) Union of Concerned Scientists ( https://www.ucsusa.org/nuclear-weapons/space-weapons/satellite-database)

7) USSPACECOM (https://www.space-track.org)

#pudebris #nasa #spacejunk #nojunk

This project has been submitted for consideration during the Judging process.