An AI Assisted Collaborative Mapping Tool for Disaster Management
- 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.
- Convolutional neural networks (CNNs)
- Transfer learning
- RAMP deep neural network
- HOTLib Python library
- 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.
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.