Predicting socio-economic status from satellite imagery using machine learning algorithm
Year: 2025
Type: conference-paper
Abstract: This thesis explores the integration of satellite imagery and machine learning to predict socio-economic status (SES) across geographic regions. Traditional SES data collection methods, like surveys and censuses, are costly and infrequent. This study demonstrates how Convolutional Neural Networks (CNNs) provide a scalable, costecient alternative for estimating SES based on landscape features visible in satellite images.
Author: Hokimjonova Zarnigora
Language: English
Subfield: Media Technology
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W6912080828
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