Projects / AI Innovation Challenge

An AI Assisted Collaborative Mapping Tool for Disaster Management

Challenge Completed!


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Objectives

  • To develop an AI-assisted mapping tool to support humanitarian organisations in mapping disaster-affected areas.
  • To improve the accuracy and efficiency of mapping efforts, especially in remote or inaccessible areas.

Methodologies

  • Convolutional neural networks (CNNs)
  • Transfer learning
  • RAMP deep neural network
  • HOTLib Python library

Outcomes

  • A deep neural network model that can accurately predict building footprints based on aerial images with an average accuracy of around 94%.
  • A Python library (HOTLib) that provides a reference implementation of the AI-assisted mapping workflow.

Impact of the solution

  • Improved accuracy and efficiency of mapping efforts in disaster-affected areas.
  • Increased availability of map data for humanitarian organisations, which can lead to better decision-making and more effective relief efforts.

Conclusion

This project developed an AI-assisted mapping tool that can help humanitarian organizations improve the accuracy and efficiency of their mapping efforts in disaster-affected areas.

Find a detailed technical case study on the project here.

Final presentation

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Requirements

Good English

A very good grasp in computer science and/or mathematics

Student, (aspiring) data scientist, (senior) ML engineer, data engineer, or domain expert (no need for AI expertise)

Programming experience with Python

Understanding of Machine Learning and Remote Sensing



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