DEPRIMAP - Unraveling the dynamics of deprived urban areas in the Majority World using AI and Earth Observation to foster evidence-based sustainable planning
DEPRIMAP aims to map, model, and analyze deprived urban areas (DUAs) in the Majority World using advanced geospatial data and machine learning techniques. The project focuses on understanding the socio-economic vulnerabilities and environmental risks faced by DUAs contributing to more resilient and sustainable urban planning.
· Buenos Aires, Argentina
· Lagos, Nigeria
· Nairobi, Kenya
· Dhaka, Bangladesh
with more cities to be included as the project progresses.
DEPRIMAP is an interdisciplinary project designed to fill critical gaps in understanding the characteristics and vulnerabilities of deprived urban areas (DUAs) in the Majority World. By leveraging diverse geospatial datasets, such as satellite imagery from Copernicus and publicly available building and population data, DEPRIMAP seeks to map and model DUAs across diverse case study cities like Buenos Aires, Lagos, Nairobi, and Dhaka. The project’s key objectives are to:
To learn more, visit the project website here.
The IDEAMAPS Network has produced an evolving family of projects with local teams in cities across the world.Our family of projects develop datasets of urban deprivation and assets (IDEAMAP Sudan, SLUMAP, IDEAtlas, and the IDEAMAPS Data Ecosystem), and foster capacity among stakeholders to use data for decision-making (Data4HumanRights, IDEAMAP Sudan).
· Buenos Aires, Argentina
· Lagos, Nigeria
· Nairobi, Kenya
· Dhaka, Bangladesh
with more cities to be included as the project progresses.
DEPRIMAP - Unraveling the dynamics of deprived urban areas in the Majority World using AI and Earth Observation to foster evidence-based sustainable planning
DEPRIMAP aims to map, model, and analyze deprived urban areas (DUAs) in the Majority World using advanced geospatial data and machine learning techniques. The project focuses on understanding the socio-economic vulnerabilities and environmental risks faced by DUAs contributing to more resilient and sustainable urban planning.
Mexico City, Mexico - Medellín, Colombia - Salvador, Brazil - Buenos Aires, Argentina - Lagos, Nigeria - Nairobi, Kenya - Mumbai, India - Jakarta, Indonesia
The primary objective of IDEAtlas is to develop, implement, validate and showcase advanced AI-based methods to automatically map and characterize the spatial extent of slums from Earth Observation (EO) data.
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