# Business Intelligence through Machine Learning from Satellite Remote Sensing Data
###### 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. satellite imagery; business intelligence; location intelligence; machine learning; small-medium enterprises +
Footnote †: journal: _Future Internet_  
There is a demand for simple, easily accessible, and user-friendly environments to leverage satellite data. Such a platform should also be open-source so that its operational overhead can be alleviated to an extent appropriate for SMEs. There are numerous examples among the field of successful open-sourced geospatial analytic tools, such as Open Data Cube. This paper aims to design and implement a proof-of-concept open-source platform that assists the decision making and management of small and medium-scale businesses, as well as policymaking, through the use of satellite data and machine learning. To prove our concept of business intelligence through remote sensing and to investigate the effectiveness of particular approaches, we focus on detecting abandoned buildings. The unregulated urbanization, the financial crisis that plagued Greece for almost a decade, and the economic implications of the COVID-19 health crisis have resulted in the emergence of disused and abandoned buildings. Abandoned buildings and unmaintained structures are a regular occurrence in urban centers. These vacant areas are responsible for increased crime rates (drug use, prostitution, etc. ):increased danger to public health and safety, since they can be prone to collapsing and fires due to deterioration, devaluation of nearby property values; and generating low property taxes, increasing costs for local governments (to secure, inspect, provide additional police and fire services, etc.). Thus, abandoned buildings contribute to a decline in a city's quality of life by providing an unappealing urban landscape for residents, visitors, and potential investors. Although there are several ways to identify vacant properties, such as driving around an area of interest, reaching out to local authorities and banks, and even advertising, they cannot be automated, require the cooperation of said parties, and can often be ineffective. This may have negative results, especially in competitive markets where finding the property ahead of the competition may be crucial for a successful investment. Thus, a system able to automatically and accurately locate a vacant property in a specific area of interest could prove to be quite helpful for investors and local or government authorities that intend to alleviate the affected neighborhoods. Furthermore, businesses could utilize the system as a means for better site selection. Business sites should, among other things, be placed in locations that forecast long-term economic growth and safety, as well as take advantage of financial incentives such as tax credits and tax breaks. So, the ability to leverage satellite data to make educated guesses when census data, such as crime rates, regional GDP, and population density, are outdated or unavailable can not only give a significant advantage over others but also reduce costs. Most related remote sensing studies mostly focused on estimating vegetation and water levels.