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.

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.

Artificial Intelligence (AI) has the power to improve our lives through a wide variety of applications, many of which fall into the healthcare space; however, a lack of diversity is contributing to limitations in how broadly AI can help people. The UCSF AI4ALL program was established in 2019 to address this issue by targeting high school students from underrepresented backgrounds in AI, giving them a chance to learn about AI with a focus on biomedicine, and promoting diversity and inclusion. In 2020, the UCSF AI4ALL three-week program was held entirely online due to the COVID-19 pandemic. Thus, students participated virtually to gain experience with AI, interact with diverse role models in AI, and learn about advancing health through AI. Specifically, they attended lectures in coding and AI, received an in-depth research experience through hands-on projects exploring COVID-19, and engaged in mentoring and personal development sessions with faculty, researchers, industry professionals, and undergraduate and graduate students, many of whom were women and from underrepresented racial and ethnic backgrounds. At the conclusion of the program, the students presented the results of their research projects at the final symposium. Comparison of pre- and post-program survey responses from students demonstrated that after the program, significantly more students were familiar with how to work with data and to evaluate and apply machine learning algorithms. There were also nominally significant increases in the students' knowing people in AI from historically underrepresented groups, feeling confident in discussing AI, and being aware of careers in AI. We found that we were able to engage young students in AI via our online training program and nurture greater diversity in AI. This work can guide AI training programs aspiring to engage and educate students entirely online, and motivate people in AI to strive towards increasing diversity and inclusion in this field.