FlareLab| 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.

FlareLab

Summary

Mapping fires around the world using data from the MODIS instrument on the NASA Terra Satelite

How We Addressed This Challenge

We developed a map that through a specific set of criteria sorts out data to display possible fires to the user. The program is constantly retrieving updated dataset from FIRMS website.

How We Developed This Project

Our original idea was to calculate possible risks of forrest fires, using machine learning trained with existing data on fires. We also wanted to use data on precipitation, vegetation & surface temperature to find a correlation between these parameteres and existing files. Unfortunately we were unable to retrieve the necessary data and read the data formats like hdf5. The only dataset we were able to use was FIRMS that used the csv format, which we converted to JSON through code in the program.

How We Used Space Agency Data in This Project

We used data from NASA's MODIS instrument

Project Demo

https://docs.google.com/presentation/d/1pKrbyvj7HjcVc9XKDWVh_8LNJ-4rQ5wtmyR0hhEJEAs/edit#slide=id.p4

Data & Resources
  • NASA FIRMS Active Fire Data:

https://firms.modaps.eosdis.nasa.gov/active_fire/#firms-txt

Tags
#Fire
Judging
This project was submitted for consideration during the Space Apps Judging process.