Skip to content

Local Chapter project

Detecting Air Pollution in Nigeria using Machine Learning and Satellite Imagery

Machine LearningSatellite ImageryRemote SensingRemote sensing - Geospatial dataComputer VisionEnvironmentWeb application
Start date
August 19, 2022
Finish date
September 30, 2022
Project status
completed
Detecting Air Pollution in Nigeria using Machine Learning and Satellite Imagery

Challenge background

Nigeria has the largest number of deaths in Africa due to air pollution. Contributors to this challenge being vehicle emissions, generator fumes, bush burning, crude oil exploration, etc.

To have a better understanding of the problem and how it can be fixed, it is important to understand which regions are affected by this the most.

The problem

Air contamination kills several people annually and Nigeria has high rates of unhealthy air quality across the African continent.

Learning about air pollution and identifying regions affected will help Nigerians better understand how to manage their health and remedy the situation.

Goal of the project

  • Get a list of regions in Nigeria suffering from air pollution.
  • Identifying regions in the country that require immediate attention by the government.
  • Identifying possible causes for the air high levels of air pollution and providing recommendations to remedy the situation.

What you'll learn

1. Processing satellite imagery 2. Carrying out image segmentation 3. Creating machine learning-driven heat maps to identify regions with high levels of air pollution

Get involved

What to expect from a Local Chapter project

First project

  • Welcomes beginners and experienced practitioners.
  • Focuses on education and collaborative delivery.
  • Produces open-source project work.

Benefits

  • Address a significant real-world problem with your skills.
  • Build your project portfolio.
  • Demonstrate your work to organizations and project partners.

Requirements

  • Working English communication.
  • A learning mindset.
  • Commitment to collaborative project work.