Explainable Population Estimation Using Deep Learning from Satellite Imagery

More than one-third of the Sustainable Development Goals (SDGs) indicators established by the United Nations (UN) are defined in terms of total population or a specific demographic sub-population [1]. Up-to-date population information of a region is crucial for decision making including access to services, distribution of vaccinations, disaster relief, and many others. Traditional population data, such as census, are not adequate for this purpose since censuses are typically conducted decennially and countries with the greatest need for up-to-date population counts conduct them even less frequently. Population estimation using alternative data sources such as satellite imagery has received significant attention in the recent years. Both census-dependent [2] and census-independent [3] approaches have been explored with some success, and many of these methods have utilized advanced image analysis methods such as deep convolutional neural networks with promising results. This project will develop these methods further, to produce sustainable, interpretable and reliable machine learning models estimating population more effectively. It will involve utilizing contextual information, both spatial and temporal, integrating satellite imagery at multiple resolutions with publicly available data sources such as land cover map, etc., combining from census, surveys and microcensus data, and explaining the decisions made by these models to the end-users.

The aim of the project is to develop sustainable, interpretable, and reliable machine learning models to effectively estimate the population of an area using satellite imagery and survey information. The project will investigate the following research questions: (1) Can contextual neighbourhood information, both short-range and long-range, improve population estimates by understanding the characteristics of the surrounding regions? (2) Can information from census, surveys, and micro-census be combined to track population reliably over time? (3) Can data from different sources and different resolutions be combined to acquire complementary information about an area? (4) Does uncertainty/bias differ in sparsely populated rural areas vary compared to densely populated urban areas? and (5) Can estimated population and associated uncertainty be explained to policymakers effectively?

Methodology

The project will incorporate satellite imagery of different resolutions and survey data using computer vision models, deep learning architecture and explainable model constructs to estimate population of a region.