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Mapping Asia Poverty from Remote Sensing
Mr REN Yongqian
(Supervisor: Prof Shunlin Liang)
Abstract:
The United Nations has set 17 Sustainable Development Goals for the 2030 Agenda, in which the elimination of poverty is the top priority. Poverty has been a pervasive global issue that demands urgent solutions for decades. The accurate mapping of its distribution is crucial for informed policy-making and effective resource allocation. Traditional methods relying on census and survey data are often time-consuming, costly, and infrequent with noteworthy data gaps. Previous studies utilizing remote sensing and other spatial data have often been limited to a single country or small regions. This proposed research integrates the daytime and nighttime remote sensing imagery with related and accessible geospatial data, combined with advanced deep learning and AI techniques, specifically, CNNs and Random Forest, to address these limitations in the broader context of Asia. This research aims to produce a precise and detailed guiding map of the poverty status in Asia to support targeted humanitarian aid, enabling more efficient resource allocation and ensuring that aid reaches the most vulnerable populations. By identifying specific regions and communities in need, this work will facilitate strategic planning and intervention, ultimately contributing to the reduction of poverty and the progress of the UN’s SDGs in Asia.
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