IDEAMAPS Network

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.

Data Production.

City-wide Degree of Deprivation
Domains (Types) of Deprivation
Insights and data visualisations

Living Data Ecosystem.

Facilitate Fair Data Exchanges
Continuously Updated Inputs & Outputs
Plug Into Existing Data Ecosystems

Learning Materials.

Direct Training and Mentorship in Pilot Cities
Tools to Launch Sub-projects in New Cities
PhD and Researcher Opportunities

Engagement & Co-design.

Communities: Data Collection & Use
Local Gvmts: Secondments & Data Usage
Modellers: Community of Practice

Innovative Modeling.

A “Just” Modelling Framework
Community of Practise for Modellers
Iterative Open Modelling

Platform Design.

Data Lake, Web Application and Mobile Application
Privacy by Design
Centring User Experience

PROJECTS WE’RE CURRENTLY WORKING ON

2024-2028
Currently Active
DEPRIMAP

·      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.

2022 - 2025
Currently Active
IDEAMAPS Data Ecosystem

LAGOS, Nigeria - NAIROBI, Kenya - KANO, Nigeria

To co-create an integrated data ecosystem that enables routine, accurate mapping of slums, informal settlements, and other deprived areas across LMIC cities 

Currently Active
IDEAtlas

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.

More Active Projects

Silo-ed Approaches to Slum Mapping

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.

01. Field-based Mapping

Field-based mapping is commonly performed by community NGOs such as Slum Dwellers International, and linked to advocacy...

Read More
02. Census & Survey

The widely cited statistic from UN-Habitat – 1 billion slum dwellers globally – is calculated by classifying and counting urban “slum ...

Read More
03. Digitising Imagery

Satellite imagery is sometimes used to manually digitize informal settlements. This approach is typically based on a...

Read More
04. Computer Models

Data scientists use computer models to semi-automatically classify deprived urban areas from satellite imagery and other...

Read More

Stay Updated.

Project Updates - News - Announcements
Subscribe to our newsletter
Thank you! Your submission has been received!
Oops! Something went wrong while subscribing.