Business Intelligence through Machine Learning from Satellite Remote Sensing Data

Christos Kyriakos and Manolis Vavalis ()
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Christos Kyriakos: Department of Electrical and Computer Engineering, University of Thessaly, 38221 Volos, Greece
Manolis Vavalis: Department of Electrical and Computer Engineering, University of Thessaly, 38221 Volos, Greece

Future Internet, 2023, vol. 15, issue 11, 1-29

Abstract: Several cities have been greatly affected by economic crisis, unregulated gentrification, and the pandemic, resulting in increased vacancy rates. Abandoned buildings have various negative implications on their neighborhoods, including an increased chance of fire and crime and a drastic reduction in their monetary value. This paper focuses on the use of satellite data and machine learning to provide insights for businesses and policymakers within Greece and beyond. Our objective is two-fold: to provide a comprehensive literature review on recent results concerning the opportunities offered by satellite images for business intelligence and to design and implement an open-source software system for the detection of abandoned or disused buildings based on nighttime lights and built-up area indices. Our preliminary experimentation provides promising results that can be used for location intelligence and beyond.

Keywords: satellite imagery; business intelligence; location intelligence; machine learning; small–medium enterprises (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2023
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