Deprived Urban Areas (DUAs) are neighborhoods where overcrowding, inadequate infrastructure, and limited access to services undermine daily life. While slums are one form of DUA, deprivation also occurs in other urban settings such as poorly serviced housing estates and rapidly expanding peri-urban areas. These communities are often located in high-risk zones and lack protective infrastructure, making them disproportionately exposed to climate hazards such as floods and heatwaves.
Despite their scale, projected to exceed 2 billion people by 2030, DUAs remain poorly represented in global datasets and urban planning systems. Many censuses are outdated or exclude informal settlements / slums, and existing global population products capture only a fraction of these populations. This data gap limits our ability to accurately assess climate risk and design targeted adaptation strategies.
What DEPRIMAP Does
DEPRIMAP is structured as a three-phase framework to systematically analyze urban deprivation and its climate implications.
Phase I – Mapping: Identify deprived urban areas using geospatial indicators derived from satellite data and other spatial data sources, capturing patterns of building density, layout, and infrastructure.
Phase II – Population: Estimate how many people live in these areas by integrating mapped extents with bottom-up spatial population modelling approaches.
Phase III – Vulnerability: Assess how exposed these populations are to climate hazards such as floods and heatwaves by combining deprivation maps with spatial hazard data.
Technical Approach
DEPRIMAP combines Earth Observation data with machine learning to analyze urban environments at neighborhood scale. The framework integrates globally available datasets on buildings, roads, land cover, and derives spatial indicators that capture urban form and infrastructure conditions. These features are used within supervised learning models to identify patterns associated with urban deprivation. Designed for scalability, the approach can be applied consistently across thousands of cities, enabling comparable assessments in data-scarce regions.
Research Outputs
2025
Preprint
The Hidden Burden of Morphological Deprivation in Small and Medium Cities
Sai Ganesh Veeravalli, Alejandro Blei, John Friesen, and 7 more authors
In growing cities, deprived neighborhoods house large numbers of residents, yet their extent and distribution remain poorly quantified, complicating implementation of SDG 11.1. 1. We present the first global, neighborhood-scale spatial estimates of morphological deprivation, covering 5,132 cities in 103 countries across Africa, Asia, and Latin America & the Caribbean (LAC) home to 3.2 billion people. Neighborhood units and built-environment indicators from the City Segments v1 dataset were combined with segment-level labels from the eight-city IDEABench benchmark to train a supervised model, which was then applied to classify each segment as morphologically deprived or non-deprived. The mapped cities contained 1.96 billion residents, of whom 349 million (17.8%) lived in deprived segments, with the highest regional shares in Africa and substantial burdens in Asia and LAC. Morphologically deprived populations spanned the urban hierarchy, with about one-third living in small and medium cities, revealing important gaps in current deprivation monitoring.
@article{veeravalli2025hidden,title={The Hidden Burden of Morphological Deprivation in Small and Medium Cities},author={Veeravalli, Sai Ganesh and Blei, Alejandro and Friesen, John and Tareke, Bedru and Kuffer, Monika and Persello, Claudio and Maretto, Raian and Abascal, Angela and Georganos, Stefanos and Thomson, Dana R},year={2025},doi={10.21203/rs.3.rs-8189204/v1},projects={deprimap},data={https://zenodo.org/records/18788260}}
Conf. Proceedings
Towards a Spatial Measure of SDG 11.1.1: Open Data for Urban Deprivation Mapping
Sai Ganesh Veeravalli, Florencio Campomanes, Sebastian Hafner, and 8 more authors
In 2025 Joint Urban Remote Sensing Event (JURSE), 2025
Urban deprivation mapping is critical for addressing inequalities and achieving Sustainable Development Goal (SDG) 11.1.1, which focuses on ensuring access to adequate housing and services in urban areas. This study introduces a geospatial framework to operationalize previously conceptualized urban Domains of Deprivation related to unplanned urbanization, limited infrastructure, and limited services within city segments at the city-scale. Leveraging open, global datasets, including Google’s V3 building footprints and 2.5D building heights, the model assigns deprivation scores (ranging from 0 to 6) based on binary thresholds derived from median values. Validation against reference slum boundaries provided by the IDEAMAPS network achieved an F1- score of 0.45 for high-deprivation areas. The results highlight the spatial distribution of deprivation across Nairobi and demonstrate the reliability of dense building indicators for identifying informal settlements. The framework demonstrates computational efficiency, enabling citywide analysis using accessible resources, and highlights its potential to inform urban planning and targeted interventions through scalable geospatial methodologies aligned with SDG 11.1.1.
@inproceedings{veeravalli2025towards,title={Towards a Spatial Measure of {SDG} 11.1.1: Open Data for Urban Deprivation Mapping},author={Veeravalli, Sai Ganesh and Campomanes, Florencio and Hafner, Sebastian and Georganos, Stefanos and Kuffer, Monika and Friesen, John and Thomson, Dana R and Ndugwa, Robert and Mwaniki, Dennis and Abascal, Angela and others},booktitle={2025 Joint Urban Remote Sensing Event (JURSE)},pages={1--4},year={2025},organization={IEEE},doi={10.1109/JURSE60372.2025.11076033},url={https://ieeexplore.ieee.org/document/11076033},projects={deprimap}}
Conf. Proceedings
Understanding Informal Settlement Transformation through Google’s 2.5D Dataset and Street View based Validation
Sai Ganesh Veeravalli, Jan Haas, John Friesen, and 1 more author
In The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2025
Monitoring change in informal settlements remains a critical challenge, particularly in data-scarce contexts across the Global South. While satellite remote sensing provides strong temporal coverage, conventional approaches for mapping the built environment often rely on very high-resolution imagery or LiDAR, which lack consistent temporal availability and are costly to scale especially for capturing vertical growth. This study leverages Google’s Open Buildings 2.5D Temporal Dataset (2016-2023), which offers annual estimates of building presence, count, and height, to detect structural change in Nairobi, Kenya. By analysing differences in building count and average height across 100-meter grid cells, we developed a rule-based framework to identify four key transformation types: vertical densification, horizontal densification, combined densification (increase in both count and height), and decline. To our knowledge, this is the first study to use this dataset to assess vertical change within informal settlements. Validation was conducted through a two-source approach using historical satellite imagery (Google Earth Pro) and archival street-level imagery (Google Street View). A total of 154 grid cells across 13 slum areas were manually assessed, yielding an overall accuracy of 96.75%. Horizontal and combined densification showed perfect agreement, while vertical densification and decline categories had over 80% accuracy. Spatial analysis across slums, adjacent buffer areas, and the broader city revealed horizontal densification as the dominant trend within informal settlements, while vertical and combined growth were more prominent in surrounding zones. These results demonstrate the potential of Google’s 2.5D dataset for scalable, interpretable urban monitoring in rapidly changing environments.
@inproceedings{veeravalli2025understanding,title={Understanding Informal Settlement Transformation through {Google's} 2.5D Dataset and Street View based Validation},author={Veeravalli, Sai Ganesh and Haas, Jan and Friesen, John and Georganos, Stefanos},booktitle={The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences},volume={48},pages={245--251},year={2025},doi={10.5194/isprs-archives-XLVIII-M-7-2025-245-2025},url={https://isprs-archives.copernicus.org/articles/XLVIII-M-7-2025/245/2025/},projects={deprimap, dyneo4slums},}
