In the Digital Cultural Heritage (DCH) domain, the semantic segmentation of 3D Point Clouds with Deep Learning (DL) techniques can help to recognize historical architectural elements, at an adequate level of detail, and thus speed up the process of modeling of historical buildings for developing BIM models from survey data, referred to as HBIM (Historical Building Information Modeling). In this paper, we propose a DL framework for Point Cloud segmentation, which employs an improved DGCNN (Dynamic Graph Convolutional Neural Network) by adding meaningful features such as normal and colour. The approach has been applied to a newly collected DCH Dataset which is publicy available: ArCH (Architectural Cultural Heritage) Dataset. This dataset comprises 11 labeled points clouds, derived from the union of several single scans or from the integration of the latter with photogrammetric surveys. The involved scenes are both indoor and outdoor, with churches, chapels, cloisters, porticoes and loggias covered by a variety of vaults and beared by many different types of columns. They belong to different historical periods and different styles, in order to make the dataset the least possible uniform and homogeneous (in the repetition of the architectural elements) and the results as general as possible. The experiments yield high accuracy, demonstrating the effectiveness and suitability of the proposed approach.

In recent years semantic segmentation of 3D point clouds has been an argument that involves different fields of application. Cultural heritage scenarios have become the subject of this study mainly thanks to the development of photogrammetry and laser scanning techniques. Classification algorithms based on machine and deep learning methods allow to process huge amounts of data as 3D point clouds. In this context, the aim of this paper is to make a comparison between machine and deep learning methods for large 3D cultural heritage classification. Then, considering the best performances of both techniques, it proposes an architecture named DGCNN-Mod+3Dfeat that combines the positive aspects and advantages of these two methodologies for semantic segmentation of cultural heritage point clouds. To demonstrate the validity of our idea, several experiments from the ArCH benchmark are reported and commented.

The number of distributed Photovoltaic (PV) plants that produce electricity has been significantly increased, and issue of monitoring and maintaining a PV plant has become of great importance and involves many challenges as efficiency, reliability, safety, and stability. This paper presents the novel approach to estimate the PV cells degradations with DCNNs. While many studies have performed images classification, to the best of our knowledge, this is the first exploitation of data acquired with a drone equipped with a thermal infrared sensor. The experiments on “Photovoltaic images Dataset”, a collected dataset, are presented to show the degradation problem and comprehensively evaluate the method presented in this research. Results in terms of precision, recall and F1-score show the effectiveness and the suitability of the proposed approach.

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Purpose This paper aims to present innovative information and communication technology (ICT) infrastructure specifically designed and optimized for the tourism sector. The case presented, “La Valle del Pensare lungo il corso del Potenza”, has been conceived with the aim of providing a digital infrastructure to ten municipalities in the Marche Region (Italy), nestled among the valley of the Potenza River. This research project is aimed at developing an important communication system that facilitates the tourist routes of mining attractions and specific thematic routes across the territory, promoting historical centers, cultural heritage, green areas and interesting places. Design/methodology/approach “La Valle del Pensare” information system has the main feature of being scalable and multi-purpose, as the contents can be managed and conveyed through the website, app mobile, totem touch screen and standard tourist signage. It is integrated and modular and allows to manage multiple information, ensuring an interoperable and multi-channel approach. It is designed for small municipalities in the province of Macerata to connect the territory’s resources and activities through a network. Findings This work represents an important communication system, i.e. innovative ICT infrastructure that facilitates the tourist routes of mining attractions and specific thematic routes across the territory. Thanks to the collection of user-generated data, the platform allows monitoring of usage statistics and performances. In this way, the municipalities can infer useful information about user’s preferences and needs. The paper also discusses how “La Valle del Pensare” gives identity to the territory, which is not identified as a simple summation of the Common, but as a recognizable system that intends to implement the level of competitiveness through the creation of a real territorial logo able to identify vocations and specificity of the Valley of the Potenza. Originality/value The value of the project lies in the ICT system, able to convey information at different scales, providing the users with updated contents; at the same time, administrations can constantly monitor its performances, being able to infer useful information about tourists’ needs, habits and preferences. The main contributions are the creation of a single cloud-based architecture for the management of multiple multi- media contents, to be exploited in various platforms; the design of a unique content management system used by several small municipalities of a same territory; the monitoring user’s preferences and needs by collecting users’ generated data; and the analysis of meaningful statistics about the tourists, tested and verified in real scenario with real users.