An interdisciplinary project that involving the use of a wide suite of remotely sensed indicators at different spatial and spectral resolutions, and the implementation of different modelling approaches, with the aim of creating a holistic and open-access tool for slum mapping at different scales, in sub-Saharan Africa.
Nairobi, Kenya - Kisumu, Kenya - Ouagadougou, Burkina Faso
STEREO-III programme of the Belgian Science Policy (BELSPO)
The overall objective of SLUMAP is to propose an open-source framework that allows for the processing of remote sensing images for (i) providing information on the location and extension of slums within a city and (ii) characterizing the physical environment within slums (i.e., in terms of greenness, built-up density, etc.) at limited cost. Our methods will be tested on a set of three sub-Saharan cities containing slums with different characteristics, namely Nairobi (Kenya), Kisumu (Kenya) and Ouagadougou (Burkina Faso).
Learn more about SLUMAP 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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