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Aerial and remote sensing archaeology are tools for identifying marks on images of archaeological remains covered by soil. In other words, they are archaeological prospection tools that fall into the category of non-destructive research methods. In this paper, a short review of these valuable research tools is carried out, presenting the way marks appear, and also the categories of aerial and remote sensing archaeology, depending on the medium of storage of images and the type of platform of transfer of sensors. The timing of the emergence of each category is determined and examples of relevant surveys are presented. Focusing on the use of an Unmanned Aircraft System (UAS) with a multispectral sensor (aerial remote sensing archaeology), their partial utilization is revealed for collecting images in areas outside of the visible spectrum, aiming at the identification of covered archaeological remains. To this end, examples of the use of UAS with different sensors are presented and indexes that have been used so far in respective applications are gathered. Aerial remote sensing archaeology took place in two areas of particular interest in Northern Greece. UAS WingtraOne GEN II was used to collect multispectral images. In both study areas, soil and vegetation or crop marks were detected, possibly covered archaeological remains, initially in RGB orthophotomosaics and then more clearly in the index maps, such as the normalized difference vegetation index, simple ratio, brightness index, second brightness index, and anthocyanin reflectance index 2b. [ABSTRACT FROM AUTHOR]

The selection of the optimal methodology for the 3D geometric documentation of cultural heritage is a subject of high concern in contemporary scientific research. As a matter of fact, it requires a multi-source data acquisition process and the fusion of datasets from different sensors. This paper aims to demonstrate the workflow for the proper implementation and integration of geodetic, photogrammetric and laser scanning techniques so that high-quality photorealistic 3D models and other documentation products can be generated for a complicated, large-dimensional architectural monument and its surroundings. As a case study, we present the monitoring of the Mehmet Bey Mosque, which is a landmark in the city of Serres and a significant remaining sample of the Ottoman architecture in Greece. The surveying campaign was conducted in the context of the 2022–2023 annual workshop of the Interdepartmental Program of Postgraduate Studies "Protection Conservation and Restoration of Cultural Monuments" of the Aristotle University of Thessaloniki, and it served as a geometric background for interdisciplinary cooperation and decision-making on the monument restoration process. The results of our study encourage the fusion of terrestrial laser scanning and photogrammetric datasets for the 3D modeling of the mosque, as they supplement each other as regards geometry and texture. [ABSTRACT FROM AUTHOR]

In the context of producing a digital surface model (DSM) and an orthophotomosaic of a study area, a modern Unmanned Aerial System (UAS) allows us to reduce the time required both for primary data collection in the field and for data processing in the office. It features sophisticated sensors and systems, is easy to use and its products come with excellent horizontal and vertical accuracy. In this study, the UAS WingtraOne GEN II with RGB sensor (42 Mpixel), multispectral (MS) sensor (1.2 Mpixel) and built-in multi-frequency PPK GNSS antenna (for the high accuracy calculation of the coordinates of the centers of the received images) is used. The first objective is to test and compare the accuracy of the DSMs and orthophotomosaics generated from the UAS RGB sensor images when image processing is performed using only the PPK system measurements (without Ground Control Points (GCPs)), or when processing is performed using only GCPs. For this purpose, 20 GCPs and 20 Check Points (CPs) were measured in the field. The results show that the horizontal accuracy of orthophotomosaics is similar in both processing cases. The vertical accuracy is better in the case of image processing using only the GCPs, but that is subject to change, as the survey was only conducted at one location. The second objective is to perform image fusion using the images of the above two UAS sensors and to control the spectral information transferred from the MS to the fused images. The study was carried out at three archaeological sites (Northern Greece). The combined study of the correlation matrix and the ERGAS index value at each location reveals that the process of improving the spatial resolution of MS orthophotomosaics leads to suitable fused images for classification, and therefore image fusion can be performed by utilizing the images from the two sensors. [ABSTRACT FROM AUTHOR]

The use of augmented reality (AR) is the only meaningful way towards the creation and integration of information into a cultural (and not only) analogue leaflet, flyer and poster, which by their nature have limited informational material. The enrichment of information, which an analogue leaflet has, with additional descriptive information (text), multimedia (image, video, sound), 3-D models, etc., gives the end recipient/user a unique experience of approaching and understanding a music event, for example. The Digi-Orch is a research project that aims to create smart printed communication materials (e.g., posters, concert programme booklets) and an accompanying AR application to provide this enhanced audience experience to classical music lovers. The paper presents the ways in which AR can extend and enrich an analogue leaflet/flyer/poster, the features of and how to use the developed AR application, as well as the components, architecture and functions of the system. It also presents the different versions of the AR application within the project, starting with the laboratory versions, continuing with the pilot versions in real conditions (music events) and reaching the final prototype and the educational version of the AR application. [ABSTRACT FROM AUTHOR]

The Archaic Acropolis of Europos (region of Central Macedonia, Greece) is an important archaeological site, which was inhabited since the 7th century BC up to the 4th century AD. In the lowland area, south of the acropolis and a short distance away, archaeologists speculate that the extensive ancient cemetery of the acropolis, which is a result of its long-term habitation, was located there. In an effort to locate marks that will support this view, WingtraOne GEN II, one of today's leading Uncrewed Aircraft Systems, was used to collect RGB and multispectral images. After the production of the necessary digital surface models and orthophotomosaics, index maps related to the crops were created. The total of the products allowed the visual identification of 123 marks, which are probably attributed to hitherto unknown covered ancient tombs. In addition, marks of unknown covered ditches of long length were found in the plain area, and on the outskirts of the acropolis marks of its possible wall and the base of one of its towers. [ABSTRACT FROM AUTHOR]

The determination of the best period for image acquisition is of major importance for research in aerial and remote sensing archaeology. This will facilitate enhanced research results by enabling the iden ti fi cation of large numbers of traces that would be otherwise unidentified. To determine the best period for satellite image acquisition in the extensive archaeological plain of Philippi (Eastern Macedonia, Greece), a systematic observation of change of intensity over time, at known places of buried archaeological structures, was performed. A low-cost image acquisition UAV was developed, which was used to acquire images every month for the period of one year. [ABSTRACT FROM AUTHOR]

Open access airborne laser scanning (ALS) data have been available in Finland for over a decade and have been actively applied by the Finnish archaeologists in that time. The low resolution of this laser scanning 2008–2019 dataset (0.5 points/m2), however, has hindered its usability for archaeological prospection. In the summer of 2020, the situation changed markedly, when the Finnish National Land Survey started a new countrywide ALS survey with a higher resolution of 5 points/m2. In this paper we present the first results of applying this newly available ALS material for archaeological studies. Finnish LIDARK consortium has initiated the development of semi-automated approaches for visualizing, detecting, and analyzing archaeological features with this new dataset. Our first case studies are situated in the Alpine tundra environment of Sápmi in northern Finland, and the assessed archaeological features range from prehistoric sites to indigenous Sámi reindeer herding features and Second Word War-era German military structures. Already the initial analyses of the new ALS-5p data show their huge potential for locating, mapping, and assessing archaeological material. These results also suggest an imminent burst in the number of known archaeological sites, especially in the poorly accessible and little studied northern wilderness areas, when more data become available. [ABSTRACT FROM AUTHOR]

Land-Use/Land-Cover (LULC) products are a common source of information and a key input for spatially explicit models of ecosystem service (ES) supply and demand. Global, continental, and regional, readily available, and free land-cover products generated through Earth Observation (EO) data, can be potentially used as relevant to ES mapping and assessment processes from regional to national scales. However, several limitations exist in these products, highlighting the need for timely land-cover extraction on demand, that could replace or complement existing products. This study focuses on the development of a classification workflow for fine-scale, object-based land cover mapping, employed on terrestrial ES mapping, within the Greek terrestrial territory. The processing was implemented in the Google Earth Engine cloud computing environment using 10 m spatial resolution Sentinel-1 and Sentinel-2 data. Furthermore, the relevance of different training data extraction strategies and temporal EO information for increasing the classification accuracy was also evaluated. The different classification schemes demonstrated differences in overall accuracy ranging from 0.88% to 4.94% with the most accurate classification scheme being the manual sampling/monthly feature classification achieving a 79.55% overall accuracy. The classification results suggest that existing LULC data must be cautiously considered for automated extraction of training samples, in the case of new supervised land cover classifications aiming also to discern complex vegetation classes. The code used in this study is available on GitHub and runs on the Google Earth Engine web platform. [ABSTRACT FROM AUTHOR]