Spot That Fire V3.0

Recent wildfires worldwide have demonstrated the importance of rapid wildfire detection, mitigation, and community impact assessment analysis. Your challenge is to develop and/or augment an existing application to detect, predict, and assess the economic impacts from actual or potential wildfires by leveraging high-frequency data from a new generation of geostationary satellites, data from polar-orbiting environmental satellites, and other open-source datasets.

Team Fire

Summary

We have been able to review in the different challenges and papers that there are many incidents of great magnitude and many people lose material and / or personal property.

How We Addressed This Challenge

We have been able to review in the different challenges and papers that there are many incidents of great magnitude and many people lose material and / or personal property.


LoRaWAN ™ is a specification for Low Power Wide Area Network (LPWAN), specifically designed for low power consumption devices, operating in local, regional, national or global networks.


We have put together a set of digital tools which allow us to carry out an awareness campaign in the shortest possible time with the help of social networks and with information from NASA's FIRMS to be able to analyze and sketch analyzes of machine learning models.

How We Developed This Project

The project is divided into 2 major implementation milestones: the digital part and the electronic part. Which can be achieved thanks to the use of Machine learning techniques with a KNN (k-Nearest Neighbor) model that allows us to predict possible safe areas for the part of fires in progress and the possibility of reports with images or videos of people surrounding the area. Additionally, for areas with higher risk, being able to deploy fixed devices that allow us to better monitor the areas of higher risk.


Electronic Part:

This analysis which will feed an app developed in Django which can be consulted through the FIRETEAM APP. In which you can see the areas with active fires and safe areas; using social networks to generate aid in the short and medium term, and thus better reduce the economic and social impact.

How We Used Space Agency Data in This Project

FIRMS Nasa

Data & Resources
  • Google Colab, 
  • Machine learning
  • Python

Server:

  • Django,
  • Postgresql, 
  • Api,

APP

  • Flutter

Graphic design

  • Illustrator
  • Premier

Social networks

Data

  • https://firms.modaps.eosdis.nasa.gov/
Judging
This project was submitted for consideration during the Space Apps Judging process.