Integrated methods for the conservation and restoration of archaeological sites. An experimental application on the "Balneum" of Piazza Dante in Catania (Italy)

Abstract. Archaeological sites in urban areas are often poorly integrated with the modern urban fabric and appear as "trenches" at a lower level than the road. They become neglected and unvalued places. The study of archaeological ruins in urban centres must involve archaeologists and architects to integrate restoration, enhancement and improvement of physical and visual accessibility projects. New digital technologies can improve these activities thanks to 3D models, "digital replicas" that allow even remote study (especially during a pandemic). The paper presents the case study of a private Roman-imperial bath in Catania. The open-air site is located at a depth of 3 metres above the road level and is not exploited. Our study consisted of historical-bibliographical research, direct and SfM surveys that allowed creating a high-resolution textured 3D model. We have extracted orthophotos and sections for geometric and technical-constructive analyses and recognition of decay from this model. We drew up an archaeological restoration and valorisation design. In addition, we imported the model into the Sketchfab portal. So, we enriched the mesh with information from the analyses employing specific tags about annotations, 2D drawings, historical and technical-scientific information. In this way, the model becomes an interactive document to monitor over time the conservation state, validate the restoration design and contribute to the valorisation of the site. This is an easy tool of exchange between all involved users (researchers, professions and students). Thus, the digital replica also represents a very high potential for dissemination purposes

Applying Fully Convolutional Architectures for Semantic Segmentation of a Single Tree Species in Urban Environment on High Resolution UAV Optical Imagery

This study proposes and evaluates five deep fully convolutional networks (FCNs) for the semantic segmentation of a single tree species: SegNet, U-Net, FC-DenseNet, and two DeepLabv3+ variants. The performance of the FCN designs is evaluated experimentally in terms of classification accuracy and computational load. We also verify the benefits of fully connected conditional random fields (CRFs) as a post-processing step to improve the segmentation maps. The analysis is conducted on a set of images captured by an RGB camera aboard a UAV flying over an urban area. The dataset also contains a mask that indicates the occurrence of an endangered species called Dipteryx alata Vogel, also known as cumbaru, taken as the species to be identified. The experimental analysis shows the effectiveness of each design and reports average overall accuracy ranging from 88.9% to 96.7%, an F1-score between 87.0% and 96.1%, and IoU from 77.1% to 92.5%. We also realize that CRF consistently improves the performance, but at a high computational cost