# Business Intelligence through Machine Learning from Satellite Remote Sensing Data
## 6 Synopsis and Future Work  
Satellite remote sensing has gained a lot of interest, with many successful applications in various different sectors and industries proving its importance. Satellite-produced data are difficult for a human to analyze and use efficiently; so, machine learning has been employed in the majority of geospatial analyses. In particular, it is widely accepted that small and medium enterprises cannot benefit from satellite-based business intelligence products. In this paper, we first offer a comprehensive and up-to-date overview of existing research efforts, results, and tools for selected related BI products. We then focus on the particular BI service of the detection of abandoned buildings in a city, a task that seems to be much more challenging than several others. More specifically, we discuss how satellite imagery and machine learning can be utilized in order to improve the sectors that greatly affect SMEs in general and the Greek economy in particular, focusing on agriculture and tourism. Apart from these, we introduced means, through satellite imagery, for better policymaking and management in Greece, which has been plagued with unregulated large-scale urbanization. Motivated by the many remote sensing and machine learning achievements, we proposed a method to detect abandoned buildings in an area of interest. Abandoned buildings mainly result from the daunting economic situation in Greece and elsewhere, and their existence can impact the performance of several businesses. Our research and development efforts are surely far from complete. They mostly focus on a single city. Nevertheless, according to our results, outlier detection seems promising for detecting vacant buildings. Our proposed open-source software can identify vacant and disused buildings in areas with different topology properties successfully, making it a great tool for Business/Location Intelligence. Small and medium-scale businesses can use it in order to validate a location for its economic sustainability (via nighttime lights) and possible dangers due to abandoned buildings, while policymakers can identify problematic areas and proceed to take necessary action. Light pollution can be described as the phenomenon of excessive or unwanted artificial light and may have multiple implications. One case of light pollution is stray light. For an optical system, stray light implies unwanted rays reaching the detecting surfaces that come from an unintended source or from an intended source that follows an unintended path. For example, you might have a dark suburban backyard where a neighbor's bright patio intrudes. This may have a serious impact on the satellite's optical payload, and there have been various studies on the suppression of stray lights in order to improve measurement uncertainties; see, for example, [66]. Significant studies have already been devoted to stray light suppression in socioeconomic studies; see [67] and references therein. We clearly see the importance of reducing stray light in obtaining more accurate results for our study. Nevertheless, taking into account the low spatial resolution of our dataset, such an effort is beyond the scope of our paper. One of our software's fundamental issues is the way we split our areas of interest using equal-sized polygons. These shapes are neither effective nor efficient, in a variety of cases, to capture the characteristics of a building, resulting in misclassification. The use of alternative shapes such as hexagons should improve the model's predictability significantly. Moreover, the current hyperparameter tuning methods are manual and thus not tested extensively. The use of Gridsearch Cross-Validation or similar but faster techniques (e.g., TuneSearchCV Bayesian Optimization) would enhance our model's accuracy and counter overfitting. Furthermore, further experimentation regarding preprocessing should be conducted in order to increase the information we can gain from free-of-cost satellite imagery.