HAP-hazard Hunters - Space Apps Challenge

HAP-hazard Hunters| 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.

High Altitude Pictures - Detecting Forest Fires From Space

Summary

Detects occurrence of wildfires around the world.

How We Addressed This Challenge

Uses machine learning to analyze the correlation of CO levels with wildfires.

How We Developed This Project

Both of us are up and coming data scientists so we wanted to participate in a data related challenge. We used the cartopy library for visualization, as well as numpy, pandas, and matplotlib. We had a significant challenge installing the basemap library since it is deprecated, and had to translate the given Python script to read the MOPLITT data into cartopy. I found it challenging to apply my knowledge for the first time. Our achievement was developing a cohesive plan and goal to address the problem.

How We Used Space Agency Data in This Project

We've only used CSA files: MOPLITT and RADARSAT

Project Demo

https://docs.google.com/presentation/d/1577ay7MLJ2CvvoSP9Pz8IUW_HacE7Ty6USES7F9t27g/edit?usp=sharing

Project Code

https://github.com/cjstewart/HAP-Hazard_Hunter.git

Data & Resources

MOPITT dataset from TERRA spacecraft

Judging

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