# 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
Common survey methods, based on monitoring via ships or airplanes, are costly and time-consuming. Moreover, currently, the estimation of bathymetry usually requires in situ depth measurements in order to train inversion models, a difficult or even impossible process for many areas. Cover maps regarding seagrass and corals have been created using machine learning and Senintel-2 MSI data to support coastal management of small islands [40]. Apart from traditional machinelearning techniques, Deep Learning techniques have been used in ocean remote sensing, making the precise, efficient, and intelligent mining of ocean data possible [41]. Ref. [42] showcases the ability of Sentinel-2 to find patches of plastic in coastal waters. Their study indicated the detectability of floating macroplastics and their distinction from naturally occurring objects (i.e., seaweed) in optical satellite data. They used a combination of the novel spectral index, the Floating Debris Index (FDI), and the Naive Bayes classifier to highlight the existence of plastic debris, with an accuracy of 86% in all their case study areas. Ref. [43] implements both unsupervised and supervised classification algorithms for the same task using multispectral Sentinel-2 remote sensing imagery from Cyprus and Greece. Their models have been developed using a mix of six reflectance data bands and the NDVI and FDI indices, which were proven to be the most effective at spotting floating plastics. Their results vary depending on the algorithms they use, with support vector regression having the highest accuracy. It should be noted though that they use a small number of grids to train and evaluate their models, possibly hindering their performance. Ref. [44] investigates the data fusion of S-2 and ICESat-2 data for vathymetric inversion. Their results were compared with data collected from an integrated lidar system called CZMIL; they had an RMSE of 0.35 m in waters with similarities in turbidity and bottom reflectivity. This demonstrates the ability of the fused imagery to estimate the depth of water of optically clear coastal waters. Apart from traditional tourism, rural tourism has gained interest among native tourists. Rural tourism can support sustainable development but also combat the impeding economic growth that comes from the extreme urbanization that has plagued Greece [26]. Ref. [45] studies the effects of rural tourism using multisource data, including satellite images. Specifically, they investigated changes and their drivers in the morphology and social evolution of the countryside from a touristification perspective. Results from their specific study area, Jinshitan, which showcased nonagricultural employment increase, support the notion that rural revitalization can be beneficial to rural communities, enabling their economic growth. Since tourists' preferences regularly shift in reaction to new risks, safety in tourist areas is of the utmost significance. Given how dangerous they can be, natural hazards must be taken into account in efforts to promote safe travel. Systems that monitor and forecast extreme natural phenomena in points of tourist and cultural interest allow for effective risk management and response. Ref. [46] integrates satellite imagery and meteorological forecasts to develop an early warning and incident response system for the protection of tourists in outdoor Greece. The system includes wildfire, flood, and extreme weather warning modules. Their findings may be applied to the creation of further natural risk management strategies for cultural and natural heritage sites. These studies and their outcomes for the implementation of satellite remote sensing in regions of touristic interest can provide valuable information for Greece's stakeholders. Tourism planners and policymakers, as well as entities of the private sector, can use satellite imagery for decision making regarding site selection, investment, and general improvement of the country's highest revenue-generating economic sector.