Business Intelligence through Machine Learning from Satellite Remote Sensing Data
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...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-10-01
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| Series: | Future Internet |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1999-5903/15/11/355 |
| _version_ | 1797459235664035840 |
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| author | Christos Kyriakos Manolis Vavalis |
| author_facet | Christos Kyriakos Manolis Vavalis |
| author_sort | Christos Kyriakos |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T16:48:33Z |
| format | Article |
| id | doaj.art-117ce55415404a66b24508b0332e25ac |
| institution | Directory Open Access Journal |
| issn | 1999-5903 |
| language | English |
| last_indexed | 2024-03-09T16:48:33Z |
| publishDate | 2023-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Future Internet |
| spelling | doaj.art-117ce55415404a66b24508b0332e25ac2023-11-24T14:43:10ZengMDPI AGFuture Internet1999-59032023-10-01151135510.3390/fi15110355Business Intelligence through Machine Learning from Satellite Remote Sensing DataChristos Kyriakos0Manolis Vavalis1Department of Electrical and Computer Engineering, University of Thessaly, 38221 Volos, GreeceDepartment of Electrical and Computer Engineering, University of Thessaly, 38221 Volos, GreeceSeveral 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.https://www.mdpi.com/1999-5903/15/11/355satellite imagerybusiness intelligencelocation intelligencemachine learningsmall–medium enterprises |
| spellingShingle | Christos Kyriakos Manolis Vavalis Business Intelligence through Machine Learning from Satellite Remote Sensing Data Future Internet satellite imagery business intelligence location intelligence machine learning small–medium enterprises |
| title | Business Intelligence through Machine Learning from Satellite Remote Sensing Data |
| title_full | Business Intelligence through Machine Learning from Satellite Remote Sensing Data |
| title_fullStr | Business Intelligence through Machine Learning from Satellite Remote Sensing Data |
| title_full_unstemmed | Business Intelligence through Machine Learning from Satellite Remote Sensing Data |
| title_short | Business Intelligence through Machine Learning from Satellite Remote Sensing Data |
| title_sort | business intelligence through machine learning from satellite remote sensing data |
| topic | satellite imagery business intelligence location intelligence machine learning small–medium enterprises |
| url | https://www.mdpi.com/1999-5903/15/11/355 |
| work_keys_str_mv | AT christoskyriakos businessintelligencethroughmachinelearningfromsatelliteremotesensingdata AT manolisvavalis businessintelligencethroughmachinelearningfromsatelliteremotesensingdata |