This repository contains the code for paper titled "User and Data-centric Artificial Intelligence for Mapping and Benchmarking Urban Deprivation for a Global Sample of Cities" https://doi.org/10.1016/j.rse.2026.115272
A web based platform which aims to map the infrastructure facilities present in the informal settlements in Freetown. Please note that this code base is now archived and this project built is now continued/supported here https://github.com/Code4SierraLeone/shdms
Python and Google Earth Engine workflows for detecting and classifying urban change using Google’s Open Buildings 2.5D Dataset, with a focus on informal settlements in Nairobi. Includes scripts for processing, typology classification, slum-level validation prep, and city-scale spatial analysis.
For spring '24. I am graduating soon thank you everyone.
City Segment Morphological Deprivation (CSMD) model: an open, reproducible workflow for preprocessing, training, applying, and validating a global urban deprivation classifier. All scripts used in the manuscript, including comparisons with SSI, MN, and WRI datasets, are included.
Guided Team Challenge 2021: Exposure Team Project
[arXiv.2602.04525] Slum-i: Semi-supervised Learning for Urban Mapping of Informal Settlements and Data Quality Benchmarking
Every building on Earth in one 69 KB HTML file — footprints, touching neighbours, height and imagery back to 2014. NARSS.
Detección de crecimiento urbano usando imagenes del satelite Sentinel-1
AI Climate: Detection of Informal Settlements
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