Monday: FINAL Fall Bootcamp Application Deadline! Apply Now

Made at Metis: Two Student Projects Focused on Natural Disaster Relief

By Emily Wilson • October 27, 2017

This post features two final projects created by recent graduates of our data science bootcamp. Take a look at what's possible in just 12 weeks. 

_____

Emily Miller
Metis Graduate
Data Scientist, Bill & Melinda Gates Foundation 

Recent news has been dominated by coverage of a wide range of troubling natural disasters, coupled with both praise and criticism for the various responses to all the damage and suffering. It's timely then, that recent Metis graduate Emily Miller's final project, Targeting Disaster Relief from Space, focused on how data science can improve response accuracy in the event of a natural disaster. 

To accomplish her project goal, she used a dataset on Typhoon Haiyan, which occurred in the Philippines in 2013, focusing on the importance of understanding which specific areas suffer the most damage after a storm in order to prioritize relief efforts. The system used now often relies on volunteers inputting information into a map that compares satellite imagery before and after a given disaster. This is time intensive and not always accurate. 

Of her project, she writes, "My goal was to create a model that could more quickly and more accurately identify the hardest hit areas in order to better target disaster relief. Using satellite imagery before and after Typhoon Haiyan in the Philippines, I built a neural network to detect damaged buildings. Using the predictions from the model, I then created density maps of damage, illustrating priority areas for relief efforts." 

So how'd the project go? What were the outcomes? How can the results be applied to real life situations? Read all about it on her blog here

_____

Daniel Licht 
Metis Graduate 

Recent graduate Daniel Licht also did his final project on a topic related to natural disaster relief. He focused on Flood Water Detection, specifically looking into the flooding that happened in Houston as a result of Hurricane Harvey. 

His goal was to build a model that, once trained, could quickly examine satellite imagery of an area, and create a 'mask' to label each pixel as either flooded or not. In a blog post about the project, he noted that in order to be useful, the model would need to be able to predict over a wide area so it could generate the type of large-scale flood extent maps that would be useful to disaster response or recovery efforts.  

Did it work? Visit Daniel's blog here to find out more about his process and results. You can also see his project slides here
_____

Want to learn more about the data science bootcamp? Check it out!


Similar Posts

alumni
The Value of an “Unstructured Mathematical Mind” in the World of Startup Data Science

By Emily Wilson • April 07, 2019

“Learn, viciously.” That's the advice Metis graduate Leon Johnson gives to those interested in the bootcamp. And he's no stranger to following his own advice when dedicated to professional and academic pursuits. In this post, read his story, which involves a Math degree, being commissioned into the Air Force, a master's degree, the bootcamp, and his current role as Data Scientist.

alumni
Finance, Technology, and Human Behavior: The Story of One Grad's Ideal Role

By Emily Wilson • March 27, 2019

Bootcamp graduate Laura Chen has always been deeply curious about human behavior. Read how she landed a job that allows her to explore that interest while also working closely with her passions for finance and technology.

alumni
Bootcamp Grad Finds a Home at the Intersection of Data & Journalism

By Emily Wilson • July 03, 2019

Bootcamp graduate Jeff Kao knows that we’re living in a time of heightened media distrust – and that’s precisely why he relishes his job in the media. “It's heartening to work at an organization that cares so much about producing excellent work,” he said of the nonprofit news organization ProPublica, where he works as a Computational Journalist. Read Kao's full story here.