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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.

Plastic and oil pollutants’ distribution and composition quantifications are vital to better understanding their effects on marine life. We present preliminary experimental results to evaluate the potential of a compact Laser-Induced Fluorescence (LIF) apparatus for the detection of plastics and oil pollutants. The apparatus is based on a comparatively inexpensive method utilizing a 405 nm laser for sample excitation in water. Fluorescence spectra are recorded from known sources of microplastic pollutants, such as polyethylene, polypropylene, polystyrene and PMMA, as well as several petroleum substances. A novel identification indicator is proposed for classifying the pollutant type in realistic environmental conditions.

Patch-based approaches in image processing are often preferable to working with the entire image. They provide an alternative representation of the image as a set of partial local sub-images (patches) which is a vital preprocessing step in many image processing applications. In this paper, a new software tool called patchIT is presented, providing an integrated framework suitable for the systematic and automatized extraction of patches from images based on user-defined geometrical and spatial criteria. Patches can be extracted in both a sliding and random manner and can be exported either as images, MATLAB .mat files, or raw text files. The proposed tool offers further functionality, including masking operations that act as spatial filters, identifying candidate patch areas, as well as geometric transformations by applying patch value indexing. It also efficiently handles issues that arise in large-scale patch processing scenarios in terms of memory and time requirements. In addition, a use case in cartographic research is presented that utilizes patchIT for map evaluation purposes based on a visual heterogeneity indicator. The tool supports all common image file formats and efficiently processes bitonal, grayscale, color, and multispectral images. PatchIT is freely available to the scientific community under the third version of GNU General Public License (GPL v3) on the GitHub platform.