We won Best Solution to AWS Disaster Response Hackathon! 🥳
Soteria uses machine learning with satellite imagery to map natural disaster impacts for faster emergency response.
Youtube Demo: https://youtu.be/frjIm_FDlhc
Devpost Home page: https://devpost.com/software/soteria-yolciw
Hugging Face ML Demo: https://huggingface.co/spaces/samt/soteria
Figma Prototype: link
Presentation Slides: link
Binary Damage Classification:
Disaster Type Classification:
Regional Damage Level Classification:
The scale, scope and intensity of natural disasters ranging from hurricanes to wildfire is only increasing as the effects of climate change worsen. The lives lost and impacted continue to highlight peoples vulnerability to these disastrous events. As a team, we wanted to use our areas of interest and expertise to serve communities who have or will be impacted by natural disasters. We don’t need to be on the ground of a disaster to make an impact. Inspired by the potential that AI has for improving the quality of life, we applied this to natural disasters. We wanted our model to be applicable to all natural disaster globally, but first we start on the East coast of Malaysia.
Download: https://xview2.org/
Learn more: https://arxiv.org/abs/1911.09296
