# Open Cities AI Challenge Dataset
This dataset was developed as part of a challenge to segment building footprints from aerial imagery. The goal of the challenge was to accelerate the development of more accurate, relevant, and usable open-source AI models to support mapping for disaster risk management in African cities [Read more about the [challenge](https://www.drivendata.org/competitions/60/building-segmentation-disaster-resilience/)]. The data consists of drone imagery from 10 different cities and regions across Africa

## Documentation
* [Link](https://radiantearth.blob.core.windows.net/mlhub/open-cities-ai-challenge/documentation.pdf)

## Tutorials
* [Segmenting Buildings for Disaster Resilience - Benchmark](https://www.drivendata.co/blog/sgementing-buildings-benchmark/) by [Azavea](https://www.azavea.com/)

## Tools & Applications
* [Open Cities AI Challenge benchmark model](https://github.com/ntarn/open-cities-ai-challenge)
* [Open Cities AI Challenge Winning Models](https://github.com/drivendataorg/open-cities-ai-challenge/)

## Publications
* [The Open Cities AI Challenge](https://towardsdatascience.com/the-open-cities-ai-challenge-3d0b35a721cc) by [Dave Luo](https://medium.com/@anthropoco)
* [Meet the Winner of the Open Cities AI Challenge](https://www.drivendata.co/blog/open-cities-disaster-winners/) by [Greg Lipstein](https://www.linkedin.com/in/greg-lipstein/)

## Creator & Contact
* [Global Facility for Disaster Reduction and Recovery (GFDRR)](https://www.gfdrr.org/en)
* njones@worldbankgroup.org

## License
* [ODbL-1.0](https://opendatacommons.org/licenses/odbl/1-0/)

## Citation & DOI
GFDRR Labs (2020). "Open Cities AI Challenge Dataset", Version 1.0, Radiant MLHub. [Date Accessed] https://doi.org/10.34911/rdnt.f94cxb