Double-A Hazard Solutions| Automated Detection of Hazards

Automated Detection of Hazards

Countless phenomena such as floods, fires, and algae blooms routinely impact ecosystems, economies, and human safety. Your challenge is to use satellite data to create a machine learning model that detects a specific phenomenon and build an interface that not only displays the detected phenomenon, but also layers it alongside ancillary data to help researchers and decision-makers better understand its impacts and scope.

Breaking thunder

Summary

We began to investigate the selected problem by addressing one of the many branches that it presented, in this case we selected the floods and thunderstorms. We started by brainstorming to see how we could tackle it, and in turn, given our technological and scientific knowledge, we proposed a solution to the challenge.

How We Addressed This Challenge

We researched all the links proposed by the space agencies and selected the one(s) that were most useful for our solution to be developed.

How We Developed This Project

We decided to develop prediction algorithms in python to anticipate a future thunderstorm or flood. At the same time we developed a web application that collects the data of these algorithms, this application is user friendly and compatible on all platforms, suitable for mobile devices such as cell phones, tablets and also in its desktop version.

How We Used Space Agency Data in This Project

Once we investigated all the sites provided by the challenge, we had a high degree of preference for these sites:



-https://worldview.earthdata.nasa.gov/

-https://earthdata.nasa.gov/eosdis/science-system-description/eosdis-components/gibs

-http://earth.jaxa.jp/en/


To simulate the mapping of storms and with that information make the prediction algorithms and web application.

Project Demo

https://docs.google.com/presentation/d/e/2PACX-1vR8rn_2xQUG4wcqvpH3xqOkW-H9n_ZVHFSoly90clcjahe8Q9aNGWpkCufsvZ8ptJzUeW2qFc9WMeNi/pub?start=false&loop=false&delayms=3000

Data & Resources

https://worldview.earthdata.nasa.gov/

https://earthdata.nasa.gov/eosdis/science-system-description/eosdis-components/gibs

http://earth.jaxa.jp/en/

Tags
#hardard #innovation #motivation #machinelearning, #data #neuralnetwork
Judging
This project was submitted for consideration during the Space Apps Judging process.