Listeners

https://drive.google.com/file/d/1pRGBxiRZBPFENp75lcNAprRf3gSHQaiD/view?usp=sharing

https://docs.google.com/presentation/d/1RBGYcARWwibrQ1Tx-CCbpbo7RB3Lw4Kd/edit?usp=sharing&ouid=109406315104168795627&rtpof=true&sd=true

To detect atmospheric water vapor and conduct corrections on the same, we used data from https://aria-share.jpl.nasa.gov/ for Nepal Earth Quakes to remove Water Vapor distortion. .kmz files from the data source are accessed on Google Earth Pro to overlay the same on the terrain. Further, two UHD quality jpg files are generated. One with the plane terrain and the second using the .kmz data, such that both files overlay and cover the same area. 

These files are then processed in jupyter notebook to subtract the water vapor noise from actual image. Hence two output images are generated. These are then plot with Matlab plot function 

Data source : https://aria-share.jpl.nasa.gov/20150425-Nepal_EQ/

Used Data : https://drive.google.com/drive/folders/1lF12gGUUj-7wCGf66zYXPXDpvJ1RoVDU?usp=sharing

Our journey with NASA Space Apps was very enjoyable and fruitful. We learned a lot about INSAR: what it is, its importance, the problems facing earth scientists in it, the ways to solve these problems, and the implementation of these methods using algorithms and ML

We chose this challenge because we enjoy Earth sciences and some of us specialize in it, so we wanted to benefit more through our participation and benefit the world by offering solutions to this challenge. 

We've used some weather evidence to confirm the proportion of water vapor in the upper atmosphere first and then determine whether or not to make a correction of the data. The debugging process starts with a classification of data using the ML model followed by the signal processing followed by the Auto-Encoder ML model

Our team had a spirit of teamwork and we worked on challenges together and enjoyed working with overall learning experience provided by this amazing project.

I would like to thank all NASA employees for their creative and innovative initiative to support and create young geniuses helping them to get insights of space and earth.

Data used : https://aria-share.jpl.nasa.gov/20150425-Nepal_EQ/

Tools used :

1. Python
2. Anaconda Navigator
3. Google Earth Pro
4. Visual Studio Code
5. Google Drive

Libraries used in Jupyter Notebook:

1. MATLAB
2. Rasterio
3. Numpy

#insar, #SAR ,#water_vapor