# Business Intelligence through Machine Learning from Satellite Remote Sensing Data
## 3 State of the Art and Related Products
### Peer Review Literature
#### 3.1.2 Urbanization: Effects on Greece and Solutions Provided by Satellite Imagery  
Urbanization in developed countries is known to be accompanied by economic expansion and industrialization [23]. While urbanization is positively correlated with economic growth, the Greek (just like in several other countries) urban system is characterized mainly by the growing dynamism of one or two metropolitan areas accommodating half of the country's population. Moreover, lack of metropolitan governance, lack of land use regulation, lack of adequate infrastructure [24], and unregulated urban development, in general, have led to multiple depriurations in the capital city of Athens, unable to compete with other major cities in the majority of sectors [25] ([[https://urbact.eu/greece](https://urbact.eu/greece)]([https://urbact.eu/greece](https://urbact.eu/greece)), accessed on 1 September 2023). It is thus mandatory to develop models that can assist policymakers in facilitating urbanization in a way that contributes to economic growth, employment growth, and environmental sustainability, rather than the pursuit of speeding up the process of urbanization [26]. Ref. [27] uses Landsat 5 TM and Landsat 8 OLI images to investigate the spatial distribution and modeling of changes in urban landscapes, as well as their economic impact. Their Markov model with NDVI masks achieved a score of 0.9 in differentiating LULC classes and correlating them with economic changes in their area of study (Lahore District). Moreover, ref. [28] takes advantage of Landsat time series characteristics and proposes a method for the extraction of economic features based on Earth's morphological changes due to regional economic growth. After collecting the Landsat data, they analyzed the correlation between economic indices and land use types. The proposed model showed the importance of construction land to infer the Gross Domain Product of an area. Although these studies prove the effectiveness of Landsat products in detecting changes in land use and land cover, worldwide-scale urbanization has brought about diverse types of urban LULC changes. These issues have been mostly understudied, with the focus of past research being on urban growth [29]. Another area that has gained interest in the past years is nighttime lights and their capability of detecting changes in LULC as well as the economy. The efforts in [29] focus on filling gaps in the literature by proposing a framework using VIIRS monthly time series to characterize diverse urban land changes. They fit the VIIRS-derived data to a Logistic-Harmonic model, taking into account the uniqueness of urban land change and the temporal information of the VIIRS time series. They produced BU area maps and disentangled the observed changes into five categories. The results show that classification based on temporal features' classification can significantly improve the accuracy of mapping regions with heterogeneous BU and NBU landscapes and promote temporal consistency and classification efficiency. Ref. [30] estimates city growth using nighttime lights. After preprocessing the data with propriety techniques in order to correct blurring, saturation, and compatibility issues with other satellite temporal and spatial resolutions, they developed a protocol to isolate stable nighttime lit pixels that constitute an urban footprint. Their measured metropolitan area size can be used along with geo-referenced population datasets (i.e., GHSL) and calculate the rate of urbanization and urban density. Although the VIIRS instrument has outperformed the DMSP OLS nighttime lights in terms of image quality and has found extensive use in urban and economic studies, the fact that it is relatively new compared with the latter means there is still space for research and improvement. Ref. [31] proves the ability of VIIRS to estimate cross-sectional and Gross Domain Product time series compared with NTLs derived from DMSP OLS across the US and metropolitan areas.