Modeling the perception and evaluation of landscapes from the human perspective is a desirable goal for several scientific domains and applications. Human vision is the dominant sense, and human eyes are the sensors for apperceiving the environmental stimuli of our surroundings. Therefore, exploring the experimental recording and measurement of the visual landscape can reveal crucial aspects about human visual perception responses while viewing the natural or man-made landscapes. Landscape evaluation (or assessment) is another dimension that refers mainly to preferences of the visual landscape, involving human cognition as well, in ways that are often unpredictable. Yet, landscape can be approached by both egocentric (i.e., human view) and exocentric (i.e., bird’s eye view) perspectives. The overarching approach of this review article lies in systematically presenting the different ways for modeling and quantifying the two ‘modalities’ of human perception and evaluation, under the two geometric perspectives, suggesting integrative approaches on these two ‘diverging’ dualities. To this end, several pertinent traditions/approaches, sensor-based experimental methods and techniques (e.g., eye tracking, fMRI, and EEG), and metrics are adduced and described. Essentially, this review article acts as a ‘guide-map’ for the delineation of the different activities related to landscape experience and/or management and to the valid or potentially suitable types of stimuli, sensors techniques, and metrics for each activity. Throughout our work, two main research directions are identified: (1) one that attempts to transfer the visual landscape experience/management from the one perspective to the other (and vice versa); (2) another one that aims to anticipate the visual perception of different landscapes and establish connections between perceptual processes and landscape preferences. As it appears, the research in the field is rapidly growing. In our opinion, it can be greatly advanced and enriched using integrative, interdisciplinary approaches in order to better understand the concepts and the mechanisms by which the visual landscape, as a complex set of stimuli, influences visual perception, potentially leading to more elaborate outcomes such as the anticipation of landscape preferences. As an effect, such approaches can support a rigorous, evidence-based, and socially just framework towards landscape management, protection, and decision making, based on a wide spectrum of well-suited and advanced sensor-based technologies.

Everyday living environments concentrate a growing amount of wireless communications leading to increased public concern for radiofrequency (RF) electromagnetic fields (EMF) exposure. Recent technological advances are turning the focus on Internet of Things (IoT) systems that enable automated and continuous real-time EMF monitoring, facing however several challenges mainly stemming from infrastructural costs. This paper seeks to provide a comprehensive view of RF-EMF levels in Greece and evidence-based decision support for a spatially prioritized deployment of an IoT RF-EMF monitoring system. We applied the stratified sampling method to estimate Electric Field Strength (EFS) in the 27MHz-3GHz range in 661 schools. Three different residential areas were considered, i.e. urban, semi-urban and rural. Results showed that the 95% confidence interval for the EFS is (0.40, 0.44) with central value equal to the sample mean 0.42 V/m. We obtained strong evidence that the mean EFS value for all Greek schools is 0.42, which is 52 times lower than the Greek safety limit and equal to 1% of international limits. Mean EFS values of individual residential areas were also significantly below safety limits. Rural areas displayed the highest EFS peaks comprising the strongest candidate to start the deployment of an IoT RF-EMF monitoring system from.

Santorini Island, located in the Southern Aegean Sea, is prone to tsunamis due to its proximity to the Hellenic subduction zone, which is one of the major tsunamigenic areas. Characteristic events, such as those of 365 A.D. and 1303 A.D. greatly affected the coasts of the Eastern Mediterranean Sea, causing significant loss of life and construction damage. Tsunami disaster risk is nowadays significantly higher due to the increased exposure of the buildings as a result of the economic and touristic growth of the Aegean Islands. This study focuses on the eastern coast of Santorini, since its morphology and human presence amplify the necessity to assess its building vulnerability. After conducting an exposure analysis at the settlements of the eastern coast, Kamari poses the highest physical, social and economic relative exposure to any potential natural hazard. The main objective of this research is to quantify the building stock’s vulnerability to tsunami hazard. For this purpose, a “worst-case run-up scenario” was developed. Considering the history of tsunamis in the Aegean Sea, an extreme sea-level rise after a 10 m a.s.l. tsunami run-up, caused by an earthquake with M_w~8.5, was assumed. The relative vulnerability of the buildings in Kamari was calculated via the application of the Papathoma Tsunami Vulnerability Assessment (PTVA-4) analytic model. The results indicate that 423 buildings are within the inundation zone, 58% of which are characterized as highly and very highly vulnerable to tsunamis, revealing the problematic characteristics of the building stock, offering important information to the decision-makers to mitigate a possible future tsunami impact.

Mining landscapes have been widely acknowledged to be perceptually intrusive. Yet, the way visual attention of actual observers is affected by such landscapes has not been subjected to objective examination until the present time. In this study, an eye tracking experiment is utilized for the first time in the surface mining domain to explore the effect of the changes in open pit mine positioning and size upon observers’ gaze patterns by utilizing eye tracking metrics and attention heatmaps. Statistical tests are also applied and both changes have been shown to significantly modulate the excavated surface’s eye-catching properties, importance and observability.

Short-term house rentals constitute a growing component of tourist accommodation in several countries and the determination of factors affecting rents is an important consideration in relevant studies. Short-term rentals have shown increasing trends in the city of Athens, Greece; however, this activity has not been adequately studied. In this paper, spatial data of Airbnb rentals in Athens are analyzed in order to indicate the factors which are important for the spatial variation of rents. Factors such as property capacity, host attributes and review characteristics are considered. In addition, several locational attributes are examined. Regression analysis techniques are used to predict the cost per night, according to various explanatory factors, while the results of two models are presented: ordinary least squares (OLS) and geographically weighted regression (GWR). The results of the OLS model indicate several factors determining the rent, including capacity and host characteristics, as well as locational attributes. The GWR model produces more accurate results with a smaller number of independent variables. For the residuals analysis several additional amenities were examined that resulted in a small impact on rents. The unexplained spatial variation of rents may be attributed to neighborhood characteristics, socioeconomic conditions and special characteristics of the rentals.