IDEATLAS

Output

Developing AI-based methods to map and characterize slums from Earth Observation data.​

Output

Project Output

The requirements and validation methods of the project outputs will be defined with Early Adopter and Stakeholders during the Co-Design Living Lab. The EO best practices were reviewed, including the capacities of satellite missions and state of the art EO algorithms to support SDG 11.1.1 data curation. The outputs framed the product development for the 8 pilot cities, and was already applied to 10 extra cities. As part of the National Demonstrations, the following products were developed for the 8 pilot cities, which are diverse in terms of geography and morphology: Extension of DUAs (binary map), Urban Deprivation index, and city-scale statistics. 

Below you can find the reports describing IDEAtlas outputs:

Cloud System

Cloud System & Web-GIS

The IDEAtlas User Portal is a web application specifically designed for the visualization of the IDEAtlas prediction results, the reference data, the city boundaries, and the Slum Severity Index Layers. Cities can be analyzed in detail using basemaps such as high-resolution satellite basemaps or OpenStreetMap. Authenticated users have the option to digitize areas by creating polygons, assigning built-up types, and defining further attributes, as well as leaving a comment. The results are stored in a database and will be used as training data to improve the IDEAtlas prediction models. Using the IDEAtlas User Portal helps to continuously build a solid knowledge base about informal settlements worldwide.

You can simply access the portal here.

Publications

Publication List

Tareke, Bedru, Paulo Silva Filho, Claudio Persello, Monika Kuffer, Raian Vargas Maretto, Jon Wang, Angela Abascal et al. “User and Data-Centric Artificial Intelligence for Mapping and Benchmarking Urban Deprivation for a Global Sample of Cities.”. https://www.sciencedirect.com/science/article/pii/S0034425726000428 

Filho, P. S., Tareke, B., Persello, C., Maretto, R. V., Machado, R., Kuffer, M., Abascal, A., & Wang, J. (2025, 5-7 May 2025). Mapping Deprived Urban Areas with an Optimized Loss-weight Feature-guided Deep Learning Model. 2025 Joint Urban Remote Sensing Event (JURSE), https://doi.org/10.1109/JURSE60372.2025.11075980

Filho, P. S., Tareke, B., Persello, C., Kuffer, M., Maretto, R., Abascal, A., Wang, J., & Machado, R. (2024, 8-10 April 2024). Feature-guided deep learning model for mapping deprived areas. 2024 International Conference on Machine Intelligence for GeoAnalytics and Remote Sensing (MIGARS), https://doi.org/10.1109/JURSE60372.2025.11075980

Samper, Jose and Pedrassoli, Julio and Espinosa, Malena Jaramillo and D’Attoli, Juan Manuel and Boanada-Fuchs, Anthony and Kuffer, Monika, Spatiotemporal Dynamics of Informal Settlements Across Argentine Cities: A National-Scale Analysis. Available at SSRN: https://ssrn.com/abstract=5588589 or http://dx.doi.org/10.2139/ssrn.5588589

Tareke, B., Filho, P. S., Persello, C., Kuffer, M., Maretto, R. V., Wang, J., Abascal, A., Pillai, P., Singh, B., D’Attoli, J. M., Kabaria, C., Pedrassoli, J., Brito, P., Elias, P., Atenógenes, E., & Santiago, A. R. (2024, 7-12 July 2024). User and Data-Centric Artificial Intelligence for Mapping Urban Deprivation in Multiple Cities Across the Globe. IGARSS 2024 – 2024 IEEE International Geoscience and Remote Sensing Symposium, https://doi.org/10.1109/IGARSS53475.2024.10640428

Tareke, B., Silva Filho, P., Persello, C., Kuffer, M., Maretto, R. V., Wang, J., Abascal, A., Pillai, P., Singh, B., D’Attoli, J. M., Kabaria, C., Pedrassoli, J., Brito, P., Elias, P., Villaseñor, E. A., Ramírez Santiago, A., Mulyana, W., Pratomo, J., Leska, R., . . . Thomson, D. R. (2025). Innovative Data Solutions for Inclusive Cities: The IDEAtlas User Portal. https://isprs-archives.copernicus.org/articles/XLVIII-M-7-2025/237/2025/isprs-archives-XLVIII-M-7-2… 

StoryMaps

Web Map Story

Team

Scroll to Top