Projects in the IDEAMAPS Network generate citywide surface maps of area deprivations and assets, and support stakeholders to use data for upgrading, advocacy, monitoring, and more.
We strive for data to be validated by city stakeholders, comparable across cities, updated routinely, and usable by community and local government stakeholders to seek equity and justice for all.
IDEAMAPS Network surface maps cover city administrative boundaries and larger built-up metropolitan areas to ensure that diverse users can access complete data for the city, however “the city”is defined.
Data outputs are Data outputs are formatted as ~100x100m grid cells to serve multiple purposes:formatted as ~100x100m grid cells to serve multiple purposes:
The IDEAMAPS Network co-designs new data and new processes of data exchange and integration by leveraging the strengths of often silo-ed “slum” mapping traditions.
We center the context knowledge of community mappers and other field experts; use scalable, reproducible modelling techniques as a vehicle; and integrate diverse social and environmental data from communities, local and national governments, NGOs, academia, and the private sector.
· 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.
.png){kind=logo}
