Pinpointing where poverty is most severe and tracking its changes over time is crucial for helping communities effectively. However, traditional benchmarks like household surveys and national censuses often fall short—they’re expensive, slow, and infrequent. In countries like Sri Lanka, this means we’re often relying on outdated information, hindering our ability to respond to sudden economic shocks or disasters. On top of that, poverty cannot be determined by income data alone, rather its multidimensional, where factors such as infrastructure, access to services, and economic activity also play a role in determining a community’s well-being. To capture these complexities, our DAP team (Data, Algorithms, and Policy) explored something different: how to rethink the way we measure poverty in Sri Lanka using AI with non-traditional data sources? 
Pinpointing where poverty is most severe and tracking its changes over time is crucial for helping communities effectively. However, traditional benchmarks like household surveys and national censuses often fall short—they’re expensive, slow, and infrequent. In countries like Sri Lanka, this means we’re often relying on outdated information, hindering our ability to respond to sudden economic shocks or disasters. On top of that, poverty cannot be determined by income data alone, rather its multidimensional, where factors such as infrastructure, access to services, and economic activity also play a role in determining a community’s well-being. To capture these complexities, our DAP team (Data, Algorithms, and Policy) explored something different: how to rethink the way we measure poverty in Sri Lanka using AI with non-traditional data sources?
Science writer Nalaka Gunawardene shares his insights following a forum held in mid-December 2021.
A research brief which explores the key data sources, algorithmic techniques and roadblocks in applying remote sensing techniques for development.
After showcasing our work at ICTD2009 (see poster), where our work: real-time biosurveillance program (RTBP) was highlighted along with Bill Gates in a Qatar media article, Prof. Artur Dubrawski (Director Auton Lab) and I returned to Sri Lanka to engage in work related to our pilot project: RTBP. Prof. Dubrawski’s visit included a workshop on T-Cube web interface in support ot the RTBP for the RTBP researchers at Sarvodaya head quarters in Moratuwa (see workshop program), a colloquium on Machine Learning in Support of Biomedical Security for the faculty and students at the University of Colombo School of Computing, and participating in the health worker m-HealthSurvey training program in Kuliyapitiya. The work under taken, April 21 – 25, is elaborated in the trip report.