# Business Intelligence through Machine Learning from Satellite Remote Sensing Data
## 3 State of the Art and Related Products
### Peer Review Literature
#### 3.1.3 Tourism Intelligence through Satellite Data  
Tourism has been a valuable source of revenue within Greek economic activity, being one of its most important economic sectors. Greece, just like many other countries around the globe, has been a major tourist destination and attraction, with the number of tourists increasing each year dramatically according to data from the World Bank, hitting a record high of 33.1 mil international arrivals in 2018 [34]. It is thus important to find ways to create an environment where tourism and tourism-related businesses can flourish while accounting for safety, enough accommodation, and easy transportation. Tourism planning and administration can benefit from quick, affordable, and easy identification of popular tourist destinations. Using open-source, Near-Real-Time data sources like social media, many studies have been conducted to examine and assess a location's tourism circumstances. Up until recently, research heavily relied on the annual stats of small sample sizes and/or on the integration of high-resolution statistical datasets and generally conventional big data sources that ignore spatial heterogeneity and drivers of tourism demand, while also being often unavailable [35; 36]. Remote sensing satellite data, which offer Near-Real-Time data over large-scale geographies and are good indicators of the economy and spatial distribution, have been used for tourism intelligence. Tourism's spatial dispersion significantly affects both its operational effectiveness and regional relevance, and the continuous satellite observation and in-depth study of nighttime lights can pave the way to clarify human activities and socioeconomic dynamics. The ability of nighttime light emission to estimate the touristic activity within European countries was examined by [37]. The correlation of touristic activity with seasonal changes in nighttime light satellite imagery collected between 2012 and 2013 from both DMSP-OLS and VIIRS was investigated. To evaluate their findings, they used statistical tourism data on the country level, which after preprocessing with GIS, were used in linear and geographically weighted regression. The results of their statistical tests show that there is a strong correlation between nighttime light emissions and tourist activity. The GWR has proven to be a useful tool for examining this relationship, but some additional factors should be taken into account before judging its ability and accuracy. In a similar fashion, ref. [38] uses OLS and GWR to investigate nighttime light's seasonal changes and their relation to tourism in 112 regions of Hunan Province, China. According to their studies' results, the intensity of luminous radiation is highly correlated with tourism points of interest. Furthermore, spatial heterogeneity and seasonal differences in tourism activities' were also observed across different regions. These findings, related to the social environment and resource allocation, can be helpful when studying tourism at the county level. Ref. [39] demonstrates the capability of nighttime lights and crowd-sourced data (i.e., OpenStreetMaps, Twitter) to detect tourism areas of interest. They generated active tweet clusters through the DBSCAN clustering algorithm to identify the touristic places where essential facilities related to travelers' needs were available. They then examined the adequacy of NTL remotely sensed data to recognize proper tourism areas in Nepal, where social media penetration is relatively low. They successfully detected important tourism areas in remote and urban regions with an F1 score of 0.72. Greek tourist resorts and attractions are distributed mainly across coastal areas. The preservation of these areas, as well as measures to ensure safety and conditions that will not hinder access by tourists, is really important. Nearshore bathymetry estimation is crucial for understanding coastal processes and enabling many industries, including offshore construction, fishery, and tourism, among others.