Predicting city poverty using satellite imagery

Journal
IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
ISSN
21607508
Date Issued
2019-06-01
Author(s)
Piaggesi, Simone
Gauvin, Laetitia
Tizzoni, Michele
Adler, Natalia
Verhulst, Stefaan
Young, Andrew
Price, Rihannan
Cattuto, Ciro
Panisson, André
Type
Resource Types::text::conference output::conference proceedings::conference paper
Scopus ID
2-s2.0-85081384542
URL
Abstract
Reliable data about socio-economic conditions of individuals, such as health indexes, consumption expenditures and wealth assets, remain scarce for most countries. Traditional methods to collect such data include on site surveys that can be expensive and labour intensive. On the other hand, remote sensing data, such as high-resolution satellite imagery, are becoming largely available. To circumvent the lack of socio-economic data at high granularity, computer vision has already been applied successfully to raw satellite imagery sampled from resource poor countries. In this work we apply a similar approach to the metropolitan areas of five different cities in North and South America, starting from pre-trained convolutional models used for poverty mapping in developing regions. Applying a transfer learning process we estimate household income from visual satellite features. The urban environment we consider is characterized by different features with respect to the resource-poor training environment, such as the high heterogeneity in population density. By leveraging both official and crowd-sourced data at city scale, we show the feasibility of estimating the socio-economic conditions of different neighborhoods from satellite data.
Funding(s)
Fondazione CRT
Subjects
computer vision

; 

developing countries

; 

population statistics

; 

remote sensing

; 

transfer learning

; 

convolutional model

; 

high heterogeneity

; 

high resolution satellite imagery

; 

population densities

; 

remote sensing data

; 

socio-economic conditions

; 

socio-economic data

; 

urban environments

; 

satellite imagery