Improving the Aftermath Management of an Earthquake

Challenge Background

California is unusually prone to earthquakes because it exists on the San Andreas Fault. The presence of hundreds of fault lines has led to over 10,000 earthquakes per year in California.

How can a city prepare to respond to an earthquake? There are many ways that a local city/ government can respond but protecting the safety and lives of individuals is the priority during any disaster situation. Emergency evacuation planning will help reduce confusion, minimize injuries, and ultimately save lives. One can use the Istanbul case study to get educated about how to devise safe paths in the aftermath of an earthquake and adapt it to any local area, including LA.

This part of the project only covers the Istanbul case study. A future project or a modification could be using data from the LA area.

The Problem

There is much work being done across the world to use AI for predicting earthquakes and damage. For example, one way to help the affected population is by accurately predicting and verifying safe routes between schools, hospitals, workplaces, and homes to reduce the risk of traveling after an earthquake. 

Goal of the Project

Partnering with USC on curriculum and capstone projects. This is one of the 4 they have chosen.

Project Timeline

What you'll learn

This is a computer vision project that will build your skills in the areas of:

1. Deep Learning Models

2. Image Segmentation Models

3. Pathfinding Algorithms

4. Satellite Image Processing

5. Integrating heatmaps with existing street graphs (OSM)