Mapping Informal Settlements in Developing Countries using Machine Learning and Low Resolution Multi-spectral Data

@article{GramHansen2019MappingIS,
  title={Mapping Informal Settlements in Developing Countries using Machine Learning and Low Resolution Multi-spectral Data},
  author={Bradley Gram-Hansen and Patrick Helber and I. Varatharajan and Faiza Azam and Alejandro Coca-Castro and V. Kopackov{\'a} and P. Bilinski},
  journal={Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society},
  year={2019}
}
  • Bradley Gram-Hansen, Patrick Helber, +4 authors P. Bilinski
  • Published 2019
  • Computer Science, Mathematics
  • Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society
  • Informal settlements are home to the most socially and economically vulnerable people on the planet. In order to deliver effective economic and social aid, non-government organizations (NGOs), such as the United Nations Children's Fund (UNICEF), require detailed maps of the locations of informal settlements. However, data regarding informal and formal settlements is primarily unavailable and if available is often incomplete. This is due, in part, to the cost and complexity of gathering data on… CONTINUE READING

    Figures, Tables, and Topics from this paper.

    Computer Vision Applications and their Ethical Risks in the Global South
    • 1
    Deriving a Quantitative Relationship Between Resolution and Human Classification Error
    Overview of Computer Vision Applications and Risks in the Global South
    • 2020

    References

    Publications referenced by this paper.
    Canonical Correlation Forests
    • 53
    • Highly Influential
    • PDF