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.

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.

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.