Seminarium - AI and Earth Observation in support of urban sustainability in the Global South
Välkommen till ett av KTH Rymdcenters månatliga seminarium, denna gång av Stefanos Georganos.
Tid: To 2023-08-31 kl 15.15 - 16.00
About the speaker:
Stefanos is an Associate Professor in Geomatics at Karlstad University and a postdoctoral researcher with KTH, Royal Institute of Technology, funded by Digital Futures. He does research in quantitative human geography, remote sensing, spatial epidemiology, demography, and machine learning. He is particularly interested in the use of geo-information for helping address the UN Sustainable Development Goals, with a geographical interest in the Global South.
His latest research unravels the potential of machine and deep learning and Earth Observation to detect, measure and characterize socio-economic inequalities in deprived urban areas in support of the most vulnerable communities. Stefanos is the co-Chair of the European Association of Remote Sensing Laboratories Special Interest Group on Developing Countries. He has coordinated and managed large international consortia such as REACT ( react.ulb.be/ ).
The Global South includes some of the most rapid-growing urban regions in the world. This ongoing shift has dramatically affected their capacity to provide essential services for their residents, such as durable housing, employment and healthcare accessibility.
On the contrary, the proliferation of deprived urban areas, further marginalizing the urban poor has been an undisputed observation of the last years. It is therefore profound, that efforts to improve the quality of life of urban dwellers are needed. A starting point to do so is to provide relevant authorities, stakeholders, and organizations with useful socioeconomic, demographic, and health indicators of the urban dwellers.
Nonetheless, in large parts of the Global South, this type of critical information is at best scarce and at worst non-existent and certainly not suited for sophisticated analyses. In this talk, the potential and challenges of harnessing the power of AI and Earth Observation tools and data to fill these data gaps are discussed. Various examples pertinent to poverty, population estimation, epidemiology, and land use are demonstrated, and a vision of future directions is unraveled.