Welcome to the SLUMAP WebGIS, where you can explore a rich collection of thematic maps that offer valuable insights into spatial information on urban deprivation. Discover citywide spatial patterns of deprivation and detailed environmental conditions of deprived urban areas (DUAs), including areas commonly referred to as 'slums'.

The user-friendly interface allows you to browse maps, perform simple spatial analyses, and download geospatial layers, providing you with reliable data and metadata to support informed decision-making and advocacy.

All vector maps have a resolution of 100 m x 100 m (when projected in UTM) and are derived from satellite imagery, open big geospatial data, and for some of them, citizen science engagement.

Citywide indicators: Citywide maps show which areas are most probably deprived (based on their morphology), the population density, and the built-up density.

Indicators for deprived areas: Within the most deprived areas, detailed maps show specific urban characteristics of deprivation (e.g., solid waste accumulation, density of open spaces, density of urban green, local citizens' perception of deprivation).

Land cover in deprived areas: Detailed raster maps show the land cover of the most deprived areas, with eight classes: buildings, ground, low vegetation, trees, shadow, vehicles, water and waste.

We will be adding maps for more cities and more themes as our research projects progress. Maps are currently available for these cities:

Whether you are a policymaker, a community member, an academic researcher, a community-based organisation (CBO) or non-governmental organisation (NGO) worker, or simply a concerned individual, the SLUMAP WebGIS can provide you with an in-depth understanding of deprived areas and their diversity.

According to UN-Habitat, about one billion people live in slums, informal settlements or deprived urban areas, and face numerous challenges related to housing, infrastructure, and access to essential services. Unfortunately, these areas often lack geospatial information, making them "invisible spaces" and hindering effective policymaking and resource allocation. Our maps aim at addressing this issue by supporting local SDG monitoring, urban planning, essential service delivery, health policy and humanitarian response.

To ensure affordability, we employ open-source software, satellite imagery and open big geospatial data. We use publicly available Sentinel-1 and Sentinel-2 images for citywide coverage, and WorldView-3 images for capturing the details of deprived urban areas.
Our mapping processes strive to deliver accurate results at the lowest possible cost. They were developed in research projects (SLUMAP, PARTIMAP, ACCOUNT, and ONEKANA), and researchers carefully checked and accounted for data uncertainties in scientific publications. For further detailed information on methods and assessments, please visit the SLUMAP website and the ONEKANA website. Check also Invisible SPACE.

The SLUMAP WebGIS is a project that brings together a dedicated team committed to making a difference: Sabine Vanhuysse, Stefanos Georganos, Monika Kuffer, Jon Wang, Angela Abascal, Eléonore Wolff

Our research and web development received financial support from the Belgian Federal Science Policy (BELSPO) under grant agreements no. SR/11/380 (SLUMAP), SR/11/217 (PARTIMAP), SR/11/405 (ONEKANA), and from the Dutch Research Council (NWO) under grant number Veni.194.025 (ACCOUNT).
Explore the SLUMAP WebGIS and unravel the complexities of deprived areas for a better future. Check our SLUMAP and ONEKANA publications, and more particularly these references:

• KUFFER, M., ABASCAL, A., VANHUYSSE, S., GEORGANOS, S., WANG, J., THOMSON, D. R., BOANADA, A. & ROCA, P., 2023. Data and Urban Poverty: Detecting and Characterising Slums and Deprived Urban Areas in Low- and Middle-Income Countries. In: MUSTAK, S., SINGH, D. & SRIVASTAVA, P. K. (eds.) Advanced Remote Sensing for Urban and Landscape Ecology. Singapore: Springer Nature Singapore.
• KUFFER, M., ALI, I. M. M., GUMMAH, A., MANO, A. D. S., SAKHI, W., KUSHIEB, I., GIRGIN, S., ELTINY, N., KUMI, J., ABDALLAH, M., BAD, M., AHMED, F., HAMZA, M., WANG, J., ELZAKI, T., GEVAERT, C. & FLASSE, C. 2023. IDeaMapSudan: Geo-Spatial Modelling of Urban Poverty. 2023 Joint Urban Remote Sensing Event (JURSE), 17-19 May 2023, 1-4.
• GEORGANOS, S., ABASCAL, A., KUFFER, M., WANG, J., OWUSU, M., WOLFF, E. AND VANHUYSSE, S., 2021. Is it all the same? Mapping and characterizing deprived urban areas using Worldview-3 superspectral imagery. A case study in Nairobi, Kenya. Remote Sensing, 13(24), p.4986.
• VANHUYSSE, S., GEORGANOS, S., KUFFER, M., GRIPPA, T., LENNERT, M. & WOLFF, E. Gridded Urban Deprivation Probability from Open Optical Imagery and Dual-Pol Sar Data, in: 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, pp. 2110-2113.
• VANHUYSSE, S., KUFFER, M., GEORGANOS, S., WANG, J., ABASCAL, A., GRIPPA, T., WOLFF, E., n.d. Putting the invisible on the map: Low-cost Earth Observation for mapping and characterizing deprived urban areas (‘slums’), in: Urban Inequalities from Space: Earth Observation in Support of the Most Vulnerable in the Majority World. (Accepted for publication).
• WANG, J., FLEISCHMANN, M., VENERANDI, A., ROMICE, O., KUFFER, M. & PORTA, S., 2023. EO+ Morphometrics: Understanding cities through urban morphology at large scale. Landscape and Urban Planning, 233, p.104691.