Improve our understanding of the relationships between urban deprivation and exposure to temperature variations and extremes
Develop methods combining advancements in EO and Citizen Science
Produce evidence of climate vulnerabilities of the urban poor, for setting priorities (most vulnerable first)
Stimulate awareness and support advocacy with data
Promote dialogue on low-cost local adaptation measures aimed at improving quality of life
More specifically
Investigate the spatial variations of air temperature within settlements through machine-learning (ML) modelling, using EO-based covariates
and sparse temperature measurements recorded with low-cost sensors (loggers) in a citizen-science approach.
Capture the high-resolution spatial patterns of thermal susceptibility within settlements through ML modelling,
using EO-based covariates, open geospatial data, and sparse micro-survey data collected in collaboration with settlement residents.
Model the variations of population density within settlements harnessing the power of deep learning modelling using EO imagery and sparse micro-survey data.
Combine the outputs to highlight areas where high exposure and high susceptibility intersect, and quantify their population.