Remote Sensing Predicts Long-term Indicators of Governance, Stability, and Well-being

Understanding a region's socio-economic conditions can inform the development of policies in both the public and private sectors. Commercial satellite imagery provides a potential avenue for abstracting socio-economic context in a quick and relatively inexpensive manner. Satellite images contai...

Full description

Saved in:
Bibliographic Details
Main Authors: Irvine, John M., Angelini, Brigid, Crystal, Michael
Format: Conference Proceeding
Language:English
Subjects:
Biological system modeling
economic analysis
Economic indicators
Feature extraction
governance
Government
imagery
Machine learning
remote sensing
Satellites
social indicators
Soft sensors
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Understanding a region's socio-economic conditions can inform the development of policies in both the public and private sectors. Commercial satellite imagery provides a potential avenue for abstracting socio-economic context in a quick and relatively inexpensive manner. Satellite images contain infrastructural and agricultural information that, in a previous study focused on Afghanistan and Botswana, provided a useful for characterizing regional socio-economic information. Previous studies have compared survey responses to imagery feature, using supervised machine learning models. Building on previous work, this study explores long-term assessments of a country. As with previous studies, image features extracted from commercial imagery form the explanatory variables for our models. In this case, however, we seek to predict annual indicators of conditions for a country as assessed by the World Bank. The models relate imagery-derived features to indicators of Political Stability, Control of Corruption, Rule of Law, Government Effectiveness, Voice and Accountability, and Gross Domestic Product using data from multiple countries.
ISSN:2332-5615
DOI:10.1109/AIPR52630.2021.9762189