MAIIA

Inter-American Development Bank

MAIIA is a library of algorithms designed to automatically identify informal urban settlements through the analysis of satellite images. It uses a pre-configured operating system image and scripts to train and apply a detection model in two easy steps, making it easy to deploy. Developed by GIM in collaboration with the Housing and Urban Development Division of the IDB, MAIIA was created to assist the National Planning Department of Colombia. Its objective is to provide a tool capable of generating and updating detailed maps of informal settlements, reducing barriers to access to artificial intelligence and allowing its use by government agencies, researchers and other stakeholders.

The development of MAIIA addresses the critical challenge of accurately mapping and understanding urban informal settlements. Traditional mapping methods are often inadequate to capture the complexity and dynamics of these areas. The need for a tool capable of analyzing vast satellite images to identify informal settlements is crucial for urban planning and policy formulation, especially in resource-limited areas. The ability to generate and update accurate maps of these settlements is essential for effective urban development strategies, resource allocation and infrastructure planning in rapidly growing urban areas.

Analyzes satellite imagery to map informal urban settlements automatically. Implements a pre-configured Docker image for easy deployment and use. Includes scripts for training AI models and applying them to new images. Utilizes open-source technologies for enhanced functionality and accessibility. Suitable for various stakeholders in urban development and housing.

Built with Python, it ensures flexibility and broad applicability. It employs machine learning and deep learning frameworks like Tensorflow to enhance its capabilities. Facilitates large-scale image processing using Satproc and UnetSeg. Provides a Docker-based solution that simplifies installation and enhances scalability. Promotes interoperability and the use of open formats in urban and housing development.