Open Data and Artificial Intelligence to Predict The Safest Path After an Earthquake

There has been a lot of research to predict earthquakes and how to increase safety during an earthquake. A question that has remained relatively unexplored is what happens after an earthquake? And how can Artificial Intelligence help?

The aftermath of an earthquake comes with a lot of challenges:

• Trying to get to a safe spot
• Organizing rescue missions
• How do parents get to their kids at school?

The last question is a big issue to tackle, which we focused on in this challenge.

Scientists predict that there will be an earthquake in Istanbul in the near future but the exact date is difficult to identify since Istanbul resides on a fault line. This means there is a possibility of major destruction.

Keeping families together and safe is the number one priority.

The problem-to-be-solved was proposed by Impacthub Istanbul, which wanted to collaborate with Omdena to leverage their AI capabilities and community to move from a broad problem statement to a functional AI solution.

Here is a non-technical summary of the challenge.

The data

We used data from Open Street Map (OSMNX). Also, we sourced data from Google maps to get 3D images of the buildings and roads and did the modeling using Unity.

Using Artificial Intelligence for earthquake response

• Getting all the building coverage details for the district working with ‘Fatih’ district
• Finding the width of streets
• Identifying the buildings and width of streets to help us calculate the risk score. The more buildings and narrow streets the lesser the safety after an earthquake. Combing building coverage and street map we calculated the risk score.
• Finding out whether districts are adjacent by querying the OSM database.
• Mapping the streets
• Deriving the risk score and the walking distance to the destination.
• To get a path from source to the destination we used Dijkstra’s algorithm combined with Q learning to find the shortest path between source and destination.

How did we go about this?

We used all the data from OSMX; the latitude, longitude data, edges, nodes.

• Calculate the building coverage and street width map
• Using OSMNX data: Interpolate the Points making up the line. Extract lon_lat coordinates of such points. Use the generated dictionary to convert each point to x_y positions in Numpy Matrix. Use the generated dictionary to convert each point to x_y positions in Numpy Matrix. Extract numpy values of all the points making up this edge/Line. Aggregate these values by: min, max, mean, median. Return aggregated values
• Calculate the risk scores based on the two factors building coverage and street width. Residential areas had a higher risk score so when they imposed on the heat map it should show that these areas are possibly not very safe after an earthquake.
• Get the district to district adjacent to map out the streets and map the risk level for the streets in order to find the shortest path.
• Use the Dijkstra algorithm to find the shortest path from source to destination.

Problems we faced

• OSMNX is not very fast we had a lot of buffer issues so we have to move only the Istanbul data to AWS S3 to get better performance.
• OSMNX doesn’t have building coverage data so we have to scrap geo data from google maps to process it through Unity to get the 3 D images of images.

The output

 

For a more technical understanding of the challenge check out this article.

How did I get involved in the challenge?

Neither I’m an expert in the aftermath management of an earthquake nor was I an expert on network analysis using Artificial Intelligence.

I had gotten to know of Omdena through a friend on Social Media.

I was inspired by their vision of Building AI for Good through a collaborative environment and wanted to be a part of their challenge.

This project was a great learning experience. I joined this challenge not knowing how to solve this problem but the collaborative learning environment made it possible.

I had an understanding that Artificial Intelligence and Machine Learning was a vast field, but this project gave me an opportunity to experience it first hand.

Collaborating with my teammates across timezones helped me to co-ordinate my schedule more effectively, as well as learn from diverse perspectives.

Thanks to a very supportive team.

This article is written by Anju Mercian.