Launched in 2020 with funding from a UK Research and Innovation grant, we grew out of workshops hosted by the African Population and Health Research Center, Slum Dwellers International - Kenya, and UN-Habitat in Nairobi in 2019.
Projects in the IDEAMAPS network engage and link stakeholders, develop datasets of urban deprivation and foster capacity among stakeholders to use data for decision making.
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.
Current methods of mapping “slum” areas take place in isolation.
IDEAMAPS aims towards integration - using strengths from each approach to build a more detailed system.
Field-based mapping is commonly performed by community NGOs such as Slum Dwellers International, and linked to advocacy...
The widely cited statistic from UN-Habitat – 1 billion slum dwellers globally – is calculated by classifying and counting urban “slum ...
Satellite imagery is sometimes used to manually digitize informal settlements. This approach is typically based on a...
Data scientists use computer models to semi-automatically classify deprived urban areas from satellite imagery and other...
.png){kind=logo}
