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The Białowieża Forest (BF), a unique ecosystem of historical significance in central Europe, has a long history of assumed human settlement, with at least 200 known archaeological sites (until 2016). This study uncovers new evidence of the cultural heritage of this unique forest area using Airborne Laser Scanning (ALS) technology combined with traditional archaeological field assessment methods to verify the ALS data interpretations and to provide additional evidence about the function and origin of the newly detected archaeological sites. The results of this study include (1) a scientific approach for an improved identification of archaeological resources in forest areas; (2) new evidence about the history of the human use of the BF based on ALS data, covering the entire Polish part of the BF; and (3) an improved remote sensing infrastructure, supporting existing GIS (Geographic Information System) systems for the BF, a famous UNESCO Heritage site. Our study identified numerous locations with evidence of past human agricultural activities known in the literature as “field systems”, “lynchets” and “Celtic fields”. The initial identification included more than 300 km of possible field boundaries and plough headlands, many of which we have verified on the ground. Various past human activities creating those boundaries have existed since the (pre-) Roman Period up to the 13th century AD. The results of this study demonstrate that past human activities in the Polish part of the Białowieża Forest had been more prevalent than previously believed. As a practical result of the described activities, a geodatabase was created; this has practical applications for the system of monument protection in Poland, as well as for local communities and the BF’s management and conservation. The more widely achieved results are in line with the implementation of the concept of a cultural heritage inventory in forested and protected areas—the actions taken specify (built globally) the forms of protection and management of cultural and environmental goods.

The archipelago of the Aeolian Islands in the Tyrrhenian Sea is a globally important natural laboratory. The archipelago, declared a UNESCO World Heritage Site for its unique geology and biodiversity, offers a unique opportunity to study plastic pollution. This study presents an initiative to assess the occurrence of plastic pellets on the beaches of five Aeolian Islands. It provides an insight into the polymer composition and the effects of degradation. Collected pellets were analyzed using stereomicroscopy and Fourier transform infrared spectroscopy (FTIR). Hierarchical cluster analysis (HCA) based on the results of the FTIR data has proved to be an effective statistical method in identifying different clusters corresponding to different degradation phases of the collected pellets. The infrared analysis identified polyethylene (80%) as the main polymer, with a small amount of polypropylene (20%). It was found that the surfaces of some pellets undergo changes during weathering that alter the polymer surfaces. By combining data on plastic pellets from the Aeolian Islands and surrounding coastal areas, we are gaining a more comprehensive understanding of the distribution patterns of microplastics. The results of the monitoring and characterization are expected to support the developing of waste management and remediation strategies for this environmentally sensitive region.

This study explores two World Heritage Sites (WHS) as tourism destinations by applying several uncommon techniques in these settings: Smart Tourism Analytics, namely Text mining, Sentiment Analysis, and Market Basket Analysis, to highlight patterns according to attraction, nationality, and repeated visits. Salamanca (Spain) and Coimbra (Portugal) are analyzed and compared based on 8,638 online travel reviews (OTR), from TripAdvisor (2017–2018). Findings show that WHS reputation does not seem to be relevant to visitors-reviewers. Additionally, keyword extraction reveals that the reviews do not differ from language to language or from city to city, and it was also possible to identify several keywords related to history and heritage; in particular, architectural styles, names of kings, and places. The study identifies topics that could be used by destination management organizations to promote these cities, highlights the advantages of applying a data science approach, and confirms the rich information value of OTRs as a tool to (re)position the destination according to smart tourism design tenets.

The paper analyses the role of gastronomic experiences as potential tourist attractions for local development. The decision to focus on a particular UNESCO site, as the Carolino aqueduct, is based on a perceived knowledge gap regarding the lack of tourists for the Carolino aqueduct. The work is based on quantitative data collection among potential visitors of the aqueduct Carolino. Altogether, 840 valid questionnaires were collected a Partial Least Squares Structural Equation Modeling (PLS-SEM) was applied. Findings reveal that that people’s motivation, supplied food, prior knowledge and past experiences influence the gastronomic experiences. The gastronomic experiences in turn affect both satisfaction with the destination and destination loyalty. Thus, gastronomic experiences should be useful path to support the tourism in a UNESCO site, as the aqueduct Carolino. When discussing issues related to the cultural heritage, the question arises as to how should enhance the cultural heritage for tourism reasons. The results of this research demonstrate that gastronomic experiences are imperative to the success of cultural heritage tourism.

The South China Karst, a United Nations Educational, Scientific and Cultural Organization (UNESCO) natural heritage site, is one of the world’s most spectacular examples of humid tropical to subtropical karst landscapes. The Libo cone karst in the southern Guizhou Province is considered as the world reference site for these types of karst, forming a distinctive and beautiful landscape. Geomorphic information and spatial distribution of cone karst is essential for conservation and management for Libo heritage site. In this study, a deep learning (DL) method based on DeepLab V3+ network was proposed to document the cone karst landscape in Libo by multi-source data, including optical remote sensing images and digital elevation model (DEM) data. The training samples were generated by using Landsat remote sensing images and their combination with satellite derived DEM data. Each group of training dataset contains 898 samples. The input module of DeepLab V3+ network was improved to accept four-channel input data, i.e., combination of Landsat RGB images and DEM data. Our results suggest that the mean intersection over union (MIoU) using the four-channel data as training samples by a new DL-based pixel-level image segmentation approach is the highest, which can reach 95.5%. The proposed method can accomplish automatic extraction of cone karst landscape by self-learning of deep neural network, and therefore it can also provide a powerful and automatic tool for documenting other type of geological landscapes worldwide.

Airborne Laser Scanning (ALS) technology can be used to identify features of terrain relief in forested areas, possibly leading to the discovery of previously unknown archaeological monuments. Spatial interpretation of numerous objects with various shapes and sizes is a difficult challenge for archaeologists. Mapping structures with multiple elements whose area can exceed dozens of hectares, such as ancient agricultural field systems, is very time-consuming. These archaeological sites are composed of a large number of embanked fields, which together form a recognizable spatial pattern. Image classification and segmentation, as well as object recognition, are the most important tasks for deep learning neural networks (DLNN) and therefore they can be used for automatic recognition of archaeological monuments. In this study, a U-Net neural network was implemented to perform semantic segmentation of the ALS-derived data including (1) archaeological, (2) natural and (3) modern features in the Polish part of the Białowieża Forest. The performance of the U-Net segmentation model was evaluated by measuring the pixel-wise similarity between ground truth and predicted segmentation masks. After 83 epochs, The Dice-Sorensen coefficient (F1 score) and the Intersect Over Union (IoU) metrics were 0.58 and 0.5, respectively. The IoU metric reached a value of 0.41, 0.62 and 0.62 for the ancient field system banks, ancient field system plots and burial mounds, respectively. The results of the U-Net deep learning model proved very useful in semantic segmentation of images derived from ALS data.

The UNESCO world heritage site Valley of the Kings or Wadi el-Moluk (وادي الملوك) near Luxor, Egypt, hosts unique burial places of Egyptian kings and royals from the New Kingdom (c. 1539–1075 BCE) and attracts about 0.5 to 2 million tourists per year. Very steep to subvertical cliffs of Thebes Limestone surround the Valley of the Kings. The rock mass is cut by frequent joints and faults making the cliff walls prone to rockfalls. However, only few rockfall debris are found in the valley, likely due to natural remobilisation by flood events and artificial clearings and excavation works that rendered the natural debris cover over the millennia. This work focuses on rockfall susceptibility and runout and makes use of new high-resolution landscape surface models utilising terrestrial laser scanning. We investigated rockfall release areas by exploring rock mass fractures at 23 cliff segments and analysed the kinematics of potential rockfalls. Furthermore, we estimated potential rockfall deposition areas with CONEFALL supported by nine numerical simulations of single rockfall events using Rockyfor3D. We found that nearly 4500 m2 (26%) of the public walking paths and 24 out of 64 tomb entrance areas locate within potential rockfall runout zones.

The third global bleaching event caused prolonged elevated sea surface temperatures from 2014 to 2017 that heavily impacted coral reefs worldwide. This study determines changes in benthic community following this bleaching event at a remote UNESCO World Heritage Site in the Western Indian Ocean. Aldabra Atoll offers a rare opportunity to study global impacts in the absence of local anthropogenic stressors. Analysis of satellite-derived temperature data indicated that Aldabra was exposed to the highest bleaching-risk intensity of the past 20 years during this bleaching event. Bleaching-risk conditions lasted from December 2015 to June 2016 close to the 4 °C-week threshold, when bleaching is expected. Benthic cover was established pre- and post-bleaching from 21 transects across two reef locations (lagoonal reef, 2 m depth; seaward reef, 5 and 15 m depth). From a pre-bleaching benthic community in which living corals and epilithic algal matrix (EAM) predominated, Aldabra’s reefs switched to an EAM-dominated community 8 months after bleaching. Soft corals declined by 93% of their overall pre-bleaching cover to < 1%. Although overall hard-coral cover was also reduced, the decline varied among depths and might indicate local adaptations of the lagoonal reef, due to greater variability in sea surface temperature compared to the seaward reef. With the exception of Isopora palifera, all taxomorphic coral groups experienced a decline following bleaching. Overall, Rhytisma experienced a near-complete extirpation, Acroporids (excluding I. palifera) and branching Poritids declined by more than 80%, Merulinidae lost ca. 60% of their pre-bleaching cover, while massive Poritids cover slightly decreased. Aldabra’s benthic community therefore underwent substantial changes following the 2014–2017 bleaching event and showed that live coral cover declines significantly even in protected areas isolated from local anthropogenic pressures.

The UNESCO World Heritage Convention of 1972 set the contemporary standard for cultural and natural conservation. Today, a place on the World Heritage List is much sought after for tourism promotion, development funding, and national prestige. Presenting case studies from across the globe, particularly from Africa and Asia, anthropologists with situated expertise in specific World Heritage sites explore the consequences of the World Heritage framework and the global spread of the UNESCO heritage regime. This book shows how local and national circumstances interact with the global institutional framework in complex and unexpected ways. Often, the communities around World Heritage sites are constrained by these heritage regimes rather than empowered by them.

Although unmanned aerial vehicle (UAV) mapping and photogrammetry have become common and relatively accessible for surveying and mapping cultural heritage sites, conducting surveys to model sites in conflict/post conflict zones remains challenging. This is particularly true for sites in a country like Afghanistan, where limited accessibility, the presence of Unexploded ordnance (UXOs), portability of the equipment, cost efficiency, as well as absence of data connectivity and Ground Control Point establishment pose major challenges. In this paper, we discuss the adopted strategy and implemented methodology to create a 3D model from both inside and outside of a section of the fortified citadel of Shahr-e Zohak which is part of the UNESCO World Heritage Property of the Cultural Landscape and Archaeological Remains of the Bamiyan Valley in Afghanistan. In particular, we examine in this paper what acquisition strategy was set for this site by going through the reasoning for selecting specific equipment and drones, the flight parameters, the camera settings, as well as how we prepared the dataset at the flight planning stage to allow merging GPS referenced data from the external flights of the UAV with non-GPS referenced data from the flights inside the domes and built structures. Succinctly, we go through the modelling strategy and parameters that have generated optimal results using both Agisoft Metashape and Bentley Itwin Capture.Our results show that using Skydio’s X10 and S2 drones and setting a low Ground to surface distance (between 1 and 5 meters) and high overlap (75%-95%) allowed us to achieve 3D models with an average accuracy of 1 millimetre per pixel for a 120m long and 30m wide section of the fortified citadel of Shahr-i Zohak. These results also show that it is indeed possible to use UAV based photogrammetry to generate 3D models that can be used for damage assessments which is particularly useful in areas where it is difficult or impossible to bring international experts or institutions to conduct this work on site. Finally, this research highlights the capabilities as well as limitations of this method and provide practical guidelines for future works in comparably challenging environments.