Uploaded October 2020 | Updated September 2026, 5 hours ago
In most countries around the world, it is tough to base policy and socio-economic decisions on data. Large governmental initiatives such as the national census are collected once every 10 years. Despite the best of intentions, they aren’t comprehensive and quickly get out of sync with reality.
Satellite imagery offers an alternate ground truth, that is accurate at high-resolution, available across periods as a time series, easily accessible, and is relatively economical. While this is a rich source of visual information, the challenge has been in processing images and generating useful insights. The advances in deep learning have helped solve this last hurdle, placing enormous power in our hands for socio-economic data analytics.
Our work is inspired by Stefano Ermon et al, who used night light as a proxy to detect poverty in Africa (http://sustain.stanford.edu/predicting-poverty). Freely available high-resolution satellite imagery was combined with ground-truth survey data and labeled data from sources like Open Street Maps. By extracting the spatial attributes, a Deep Learning architecture was used to identify useful features from the maps such as buildings, tree cover, water bodies, and population density. This was used to arrive at important insights that could drive policy and socio-economic decisions.
A real-world implementation of this work will be presented with a live demo of the results. The promising areas of application will be discussed to illustrate the potential to save lives.
In most countries around the world, it is tough to base policy and socio-economic decisions on data. Large governmental initiatives such as the national census are collected once every 10 years. Despite the best of intentions, they aren’t comprehensive and quickly get out of sync with reality.
Satellite imagery offers an alternate ground truth, that is accurate at high-resolution, available across periods as a time series, easily accessible, and is relatively economical. While this is a rich source of visual information, the challenge has been in processing images and generating useful insights. The advances in deep learning have helped solve this last hurdle, placing enormous power in our hands for socio-economic data analytics.
Our work is inspired by Stefano Ermon et al, who used night light as a proxy to detect poverty in Africa (http://sustain.stanford.edu/predicting-poverty). Freely available high-resolution satellite imagery was combined with ground-truth survey data and labeled data from sources like Open Street Maps. By extracting the spatial attributes, a Deep Learning architecture was used to identify useful features from the maps such as buildings, tree cover, water bodies, and population density. This was used to arrive at important insights that could drive policy and socio-economic decisions.
A real-world implementation of this work will be presented with a live demo of the results. The promising areas of application will be discussed to illustrate the potential to save lives.
![Is There Life After Death? moderated by John Cleese - 2018 Tom Tom Fest [CLIP w/ Bruce Greyson]](https://i.ytimg.com/vi/jDH7Yn6ZqgQ/mqdefault.jpg "Is There Life After Death? moderated by John Cleese - 2018 Tom Tom Fest [CLIP w/ Bruce Greyson]
Neurobehavioral Scientist Bruce Greyson, M.D. describes the analogous recollections of those who’ve had Near Death Experiences (NDEs). Most commonly reported are heightened mental abilities, accurate perceptions from outside the body, and meetings with the deceased.")
