Many studies are pointing to the fact that cities are experiencing higher temperatures than non-built-up areas. Yet limited can be found on thermal inequalities in the context of vulnerable groups, specifically linked to people living in deprivation. Here, we study heat patterns across vulnerable groups living in deprivation as an important effort that should be paralleled to the other urban climate studies and aim at answering two primary questions: (1) how temperature varies within and across deprived areas, and (2) what the key driving factors are for such variation. We conduct intensive in-situ measurements by involving local residents in air temperature traverse across deprived neighbourhoods and modelling the pattern of air temperature with spatial covariates. We also compare different modelling techniques while securing the interpretability of the air temperature pattern by using understandable spatial covariates, which is especially informative for mitigation and adaptation, and linking scientific exploration and practical solutions.

This study explores the potential of a synergistic approach combining Sentinel-2 data and Google Open Buildings (GOB) for mapping urban deprivation over large areas at a 100m spatial resolution. Urban deprivation, including slums, is a crucial aspect of Sustainable Development Goal 11 (SDG): Make cities and human settlements inclusive, safe, resilient and sustainable. Using a Convolutional Neural Network (CNN) and a VGG19 architecture, we experimented with pre-training, self-training, and post-processing using OpenStreetMap to improve classification accuracy. Our results show that combining outputs from both Sentinel-2 and GOB models improves the overall model performance with an F1 score of 81%. The incorporation of post-processing is useful for the final map creation, particularly in correcting for mis-classifications in areas with obvious morphological similarities to deprived areas, such as markets. This approach, which reduces known errors in the model, holds promise for advancing the precision and reliability of urban deprivation mapping over extensive geographical areas.

IDEAMAPS is developing a participatory data-modelling ecosystem to produce maps of deprived areas ("slums") routinely and accurately at scale across cities in lower- and middle-income countries (LMICs). The Ecosystem is co-designed with local stakeholders with the aim of supporting decision-making and evidence-based policymaking. Within the IDEAMAPS Ecosystem, deprivation (multi-dimensional poverty) is modelled via a large set of subdomains (e.g., irregular layouts, waste accumulation) relating to the domains of deprivation (e.g., unplannedness, contamination). Earth Observation (EO) and AI are used to build these models at scale in environments where data is commonly absent. Models are built within intense community engagements using a Participatory AI approach, where empowerment and transparency about inputs, models and outputs are essential. Open and free EO data (e.g., Copernicus) and data with research access (e.g., SDGSat-1) are combined with public, official, and community-generated datasets to produce gridded surface maps of deprivation across cities. These outcomes support bottom-up planning and the localization of SDGs.

The rapid urbanization in many regions worldwide results in the proliferation of deprived urban areas, also known as slums or informal settlements. Our study addresses the pressing need for accurate information by investigating User and Data-centric Artificial Intelligence (AI)-based methods for mapping deprived urban areas and extracting information supporting the Sustainable Development Goals (SDG) Indicator 11.1.1. In collaboration with local communities and several (inter)national stakehlders, we co-designed AI strategies based on free or low-cost Earth Observation (EO) and geospatial data to map informal settlements in eight cties across the globe. The AI methods design, data collection, and validation strategies follow an iterative and agile process consisting of progressive refinement stages necessary to collect reliable labeled data and take user requirements into their centre. Our findings indicate that the combination of Sentinel-2 and morphometric features yields the most accurate results.

Africa, as a major climate change hotspot, faces severe impacts, including extreme temperatures. Notably, urban areas are unequally affected by these impacts. The urban poor are particularly vulnerable to extreme temperatures, because of the environmental and physical characteristics of their neighbourhoods, and their limited resources to develop coping strategies. Limited knowledge exists of the spatial patterns of thermal inequalities within neighbourhoods. Our overall scientific objective is to explore the potential of Earth Observation (EO) to study how and why urban dwellers in the Global South (focusing on Africa) with different levels of deprivation are divergently exposed to varying temperatures and extreme heat, and to quantify the urban population exposed to such conditions. We make use of several state-of-the-art EO/AI models, and employ innovative in situ data collection methods together with local stakeholders through Citizen Science. We rely as far as possible on open or low-cost satellite imagery (e.g., Sentinel-1/2, Landsat, ECOSTRESS) for scalability and transferability, and we implement Machine Learning (ML) methods, including Deep Learning (DL). Results highlight significant local differences in thermal exposure, emphasizing the need to understand and communicate these spatial patterns to support the development of cost-effective adaptation strategies.

Mapping deprived urban areas in low- and middle-income countries is essential for policy development. While urban deprivation is a complex concept encompassing multiple dimensions, we propose an approach to capture its physical traits reflected in urban morphology, aiming for scalability. Our method makes use of affordable Earth Observation imagery and existing open geospatial datasets, and eliminates the need for manual labeling. It involves feature extraction, unsupervised learning, and pseudo-label based semi-supervised learning, resulting in 'soft' urban deprivation maps that avoid flagging areas as 'slums'. The study demonstrated its effectiveness in identifying the urban types associated with deprived areas at the scale of a large sub-Saharan African city.

Population living in deprived conditions continues to grow, highlighting the urgent need for accurate high-resolution maps and detailed statistics to plan interventions and monitor changes. Unfortunately, data on deprived areas or "slums" is often unavailable, incomplete, or outdated. Leveraging satellite imagery can offer timely, and consistent information on deprived areas over large area However, there are limited studies that use free and open source data that can be used to map deprived areas over large areas and across multiple cities. To address these challenges, this study examines a scalable and transferable modeling approach to map deprived areas using contextual features extracted from freely available Sentinel-2 data. Models were trained and tested on three Sub-Sahara cities: Lagos Nigeria, Accra Ghana, and Nairobi, Kenya. The results indicate that models in individual city achieved F1 scores from 0.78-0.95 for the three cities. Additionally, the results indicate that the proposed approach may allow for the ability to transfer models from city to city allowing for large area and across city mapping.

The number of people living in deprived urban areas within low and middle income countries (LMICs) is large and predicted to continue to grow. Mapping these areas over time and space in a consistent manner is important for monitoring the Sustainable Development Goals (SDGs) and degree of deprivation between cities. This work describes the development of a mapping ecosystem to do this. The first steps are to define urban extents and collect the data needed for developing deprived area models. The goal of the mapping ecosystem is to produce maps of ranges in deprived areas that are comparable between cities and can be used by local governments and researchers to map within city variations in deprivation. By developing and describing this ecosystem, we hope to replicate this in other cities and countries so that this type of work can be expanded.

Most research pertaining to mapping deprived urban areas is limited to locating and delineating deprived area's extents within and across cities. In this work, we go beyond and characterize deprived areas by utilizing a wide suit of remotely sensed predictors to map the intra-urban distribution of land cover (LC) in deprived communities in Nairobi, Kenya. We assess the contribution of WorldView-3 (WV-3) multispectral and shortwave infrared bands for the task of deprived urban areas land cover classification at a very-high-resolution scale. Our results highlight the potential of WV -3 to accurately map the LC while the potential of intra-urban transferability was shown to be satisfactory. Moreover, feature selection dramatically decreased the computational complexity of the LC models with no losses in classification accuracy. We propose a set of indicators such as the density of garbage piles to be extracted at an aggregated grid level. This aggregation helps characterize urban deprivation at a fine scale and assist local authorities and stakeholders in implementing evidence-based policy making.

A continuously growing number of urban inhabitants in Low- and Middle-income Country (LMIC) cities live in deprived areas. Such areas are under-serviced and characterized by poor living and environmental conditions, where the unplanned morphology interacts with physical hazards. While such areas proliferate, climate change has increasingly severe impacts on them. Deprived areas are often located in high-risk zones, e.g., flood zones. However, the absence of global databases on such areas is an obstacle to locate and prioritize hotspots of deprived communities exposed to climate change. Consequently, quantifying the numbers of exposed inhabitants is not possible, though it is required in support of the Sustainable Development Goals (SDGs) (e.g., 11, 13) and local adaptation strategies. Earth observation (EO) that allows producing such data fall short of providing city-level information due to computational constraints and unsolved methodological challenges related to scalability and transferability. This paper presents a framework to combine EO data with data on hazards (e.g., storms, floods) that impact urban areas and, in particular, deprived communities. First results in pilot cities show that deprived communities are systematically more exposed to physical hazards as compared to formal built-up areas. These problems are expected to intensify in the context of climate change, as most hazards will increase in their severity and frequency.

Earth Observation (EO) to produce policy-driven information on slums has been receiving increasing attention amongst experts. However, the geo-ethical concerns associated with making slum information publicly available are commonly neglected among the EO community. This study analysed the geo-ethics in terms of technology, product, and application-level using topic-focused interviews in the Greater Accra Region, Ghana. We identified that potential users have little knowledge of machine learning-based slum mapping methods, which implies the need for technology and product documentation to improve the acceptability and usability of EO data. We observed an application mismatch among institutions. While NGOs and research institutions required data for pro-poor initiatives, most government institutions needed data for slum eradication. Such mismatches require a rethinking of how slum data should be made public. We present a guide to disseminate information to users in support of developing a global slum data repository.

With rapid urbanization leading to the proliferation of deprived urban areas (often referred to as “slums”) in sub-Saharan Africa, there is a growing number of city dwellers living in inadequate housing conditions and being exposed to multiple hazards. In this context, Earth Observation has the potential for filling gaps in spatial data availability and thereby support evidence-based policy making. We assess the potential of free open-source software, open dual-pol SAR and optical imagery (Sentinel-l and Sentinel-2), and open global datasets for producing accurate city-scale maps of areas having morphological characteristics of deprivation. Implementing a grid-based machine learning approach, we evaluate different combinations of spectral and spatial Sentinel features, and features from global data. The results show that a high accuracy can be reached with the best combinations. Since publishing maps with hard labels (e.g., deprived vs. non-deprived areas) could raise ethical concerns or even lead to misuses, the output is provided as gridded morphological deprivation probability maps.

In many cities of the Global South, informal and deprived neighborhoods, also commonly called slums, continue to proliferate, but their locations and dwellers' socio-economic status are often invisible in official statistics and maps. Very high resolution (VHR) satellite images coupled with deep learning allow us to efficiently map these areas and study their socio-economic and spatio-temporal variability to support interventions. This paper investigates a deep transfer learning approach based on convolutional neural networks (CNN) to identify the socio-economic variability of poor neighborhoods in Bangalore, India. Our deep network, pre-trained on a slum classification data set, is tuned towards the prediction of a continuous-valued socio-economic index capturing multiple levels of deprivation. Experimental results show that the CNN-based regression model can explain the socio-economic variability with an R2 of 0.75. The use of additional publicly available geographic information layers allow us to spatially extend the analysis beyond the surveyed deprived area data samples to uncover city-wide patterns of socio-economic inequalities.