This project is part of the DEPRIMAP research (Mapping and Modelling Deprived Urban Areas to Support Risk Reduction and Resilience Planning in the Global South), a FORMAS-funded project hosted at Karlstad University. DEPRIMAP aims to improve the understanding of urban deprivation through the integration of geospatial data, spatial modelling, and machine learning techniques. The project focuses on developing transferable methodologies to map and characterise deprived urban areas (DUAs), ultimately supporting local governments and stakeholders in risk reduction and resilience strategies. This specific sub-study contributes to DEPRIMAP by operationalising an expanded version of the ISLAND model - a geospatial framework that quantifies spatial deprivation across multiple dimensions, including morphology, infrastructure, accessibility, environment, and population characteristics. While the original ISLAND Model was tested in selected cities, this study aims to scale the approach to over 80 countries across Global South, encompassing thousands of cities and urban clusters with diverse spatial and socio-economic conditions. To achieve this, we will integrate and analyse large volumes of spatial data: including vector datasets (e.g., building footprints, road networks, land parcels/city blocks), raster datasets (e.g., population, NDVI, land surface temperature), and remote sensing products. Morphometric indicators will be computed using python-based packages such as momepy, while statistical and spatial operations will be performed using jupyter-based workflows. The outputs of this large-scale analysis will include geospatial indicators of urban deprivation, maps of structural inequalities, and cross-country comparisons that can inform both scientific understanding and policy decision-making. The significance of this work lies not only in its scientific contribution to urban studies and spatial deprivation modelling but also in its operational potential: the ability to support standardized, scalable, and reproducible assessments of urban inequalities using open-source data and methods. The project will generate openly accessible datasets and documented processing pipelines that may benefit researchers and practitioners working on urban resilience, poverty mapping, and sustainable development.