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.
Predicting City Poverty Using Satellite Imagery / Simone Piaggesi, Laetitia Gauvin, Michele Tizzoni, Ciro Cattuto, Natalia Adler, Stefaan Verhulst, Andrew Young , Rhiannan Price, Leo Ferres, Andre Panisson. - ELETTRONICO. - (2019), pp. 90-96. (Intervento presentato al convegno IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops tenutosi a Long Beach, California nel 16-20 June 2019).
Predicting City Poverty Using Satellite Imagery
Simone Piaggesi;
2019
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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
