Inspiration

Inefficiency in disaster response in low income countries, particularly in rural areas, causes tremendous loss because of difficulties in communication. And it is becoming even severer as global climate change continues.

Our idea is to improve disaster detection, evacuation and rescue process by analyzing satellite images.

What it does

Detect areas affected by disasters (so far only flood is supported)

How I built it

Built it using transfer learning, a technique used in machine learning

Challenges I ran into

Scrapping the web for satellite images Fine tuning parameters on a small dataset

Accomplishments that I'm proud of

Getting it to work in a day!

What I learned

Supervised techniques require a lot of data focus on unsupervised techniques for next steps

What's next for

Extend the model for earthquakes, eruptions, man-made disaster, wild-fires etc. Localizing detection to help in finding access routes. Divide areas with severity of event to allow for proper resource allocation

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