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The creation of Building Information Models (BIM) is driven by cutting-edge software applications, plug-ins, and APIs that constitute the backbone of BIM authoring tools. While free tools and APIs offer visualization and customization options, geometric modelling remains largely restricted to interactive work and proprietary platforms, which sometimes limits flexibility and efficiency. There are still only a few comprehensive workflows that fully automate the reconstruction of building elements from reality-based surveyed data. This paper introduces an innovative reconstruction pipeline developed for the Scan-to-BIM Challenge at the CVPR 2024 Workshop, where it achieved second place in the competition. A deep learning (DL)-driven BIM Module for parametric IFC reconstruction is designed to accurately reconstruct both primary and secondary building elements within a BIM framework, starting from unstructured point cloud data captured via Terrestrial Laser Scanning (TLS). By leveraging DL techniques, particularly Convolutional Neural Networks (CNNs) and Transformers Networks (PTv3), our approach uses late fusion instance segmentation across both 2D and 3D modalities to accurately identify and reconstruct class-specific elements. The pipeline ultimately generates Industry Foundation Classes (IFC) elements, enhancing modelling accuracy, parameter estimation, and consistency in subsequent stages. Results highlight the pipeline’s strong performance on various datasets, underscoring the crucial role of DL in advancing Scan-to-BIM workflows.

In many countries, recent boosts in the construction and renovation sectors and energy efficiency directives are driving a growing interest in the built environment among designers and maintainers. In this context, customized software solutions tailored for Building Information Modelling (BIM) and Building Energy Modelling (BEM) are proving to be indispensable for optimizing operational efficiency within the Architecture, Engineering, Construction, Owner, and Operator (AECOO) sector and for facilitating the generation of buildings' Digital Twins (DTs). These DTs rely on accurate geometry and ancillary information (semantics, sensors, etc.) to define properties of single elements, enabling crucial simulations in structural conditions or energy needs. However, BIM and BEM model creation and their enrichment with semantic information are highly time-consuming and prone to manual errors. Hence, there is an increasing demand for automatic methods featuring a high level of geometric accuracy to reconstruct building elements, such as walls, floors, and openings captured via 3D reality-based surveying. This paper introduces an automated method for creating Boundary Representation (B-Rep) models from 3D surveying data for the generation of digital building replicas. The method is based on the detection and computation of topological elements from 3D reality-based point clouds. It proves valuable for architectural or design workflows and for conducting energy or quality system simulations.

Pasta, a Mediterranean diet staple, enhances well-being when enriched with healthy ingredients like durum wheat-germ (WG) and wheat-bran (WB). We studied the nutritional and clinical responses to four experimental pastas (EP1-EP4) made with de-oiled WG, WB, and microencapsulated durum wheat-oil (mWO), compared to a control pasta of water and semolina dough. WG addition significantly boosted total phenols and radical scavenging activity. Simulated colonic fermentation showed WG-enriched pasta enhanced short-chain fatty acids production.The clinical response to pasta was studied in 70 healthy subjects by semiquantitative scales of sensory perception, functional ultrasonography of gastric and gallbladder kinetics, and breath test for orocecal transit time. Sensory analysis revealed differences in odor, aftertaste, and overall pleasantness, especially in EP2. Gastrointestinal motility was similar across pastas, but EP3 had a shorter transit time and higher colonic fermentation. This study suggests that wheat by-products enriched pastas offer nutraceutical benefits, agreeability, digestibility, and sustainability.

Cityscapes contain a variety of objects, each with a particular role in urban administration and development. With the rapid growth and implementation of 3D imaging technology, urban areas are increasingly surveyed with high-resolution point clouds. This technical advancement extensively improves our ability to capture and analyse urban environments and their small objects. Deep learning algorithms for point cloud data have shown considerable capacity in 3D object classification but still face problems with generally under-represented objects (such as light poles or chimneys). This paper introduces the ESTATE dataset (https://github.com/3DOM-FBK/ESTATE), which combines available datasets of various sensors, densities, regions, and object types. It includes 13 classes featuring intensity and/or colour attributes. Tests using ESTATE demonstrate that the dataset improves the classification performance of deep learning techniques and could be a game-changer to advance in the 3D classification of urban objects.

In the last few years, data fusion has been an active research topic for the expected advantages of exploiting and combining different but complementary techniques for 3D documentation. The data fusion process consists of merging data coming from different sensors and platforms, intrinsically different, to produce complete, coherent, and precise 3D reconstructions. Although extensive research has been dedicated to this task, we still have many gaps in the integration process, and the quality of the results is hardly sufficient in several cases. This is especially evident when the integration occurs in a later stage, e.g., merging the results of separate data processing. New opportunities are emerging, with the possibility offered by some proprietary tools to jointly process heterogeneous data, particularly image and range-based data. The article investigates the benefits of data integration at different processing levels: raw, middle, and high levels. The experiments are targeted to explore, in particular, the results of the integration on large and complex architectures.

For the most diverse practical purposes and communication needs (from trade to conquests or exploratory voyages), man has always represented space through the production of maps. There are countless varieties of themes, styles and graphic languages, types of representation (pictorial maps, bird's eye views, cadastral and topographic maps) used throughout history to depict, in particular, the landscape and the urban fabric. The informative, historical, cultural and identity value of this cartographic heritage, now preserved in archives and institutions around the world, is inestimable. Due to the need to preserve its content from the ravages of time and to allow its "digital" use, an increasingly intense campaign of digitization of these sources has been launched in recent years (as has happened, for example, for other historical data, such as photographs and books). The growing availability of digital historical maps and, at the same time, the notable progress in data processing and analysis, today offers new perspectives for the use and valorization of these resources. In the case, in particular, of ancient cartography, there are increasingly broader scenarios for accurate studies on the evolution and transformation of urban structures, to support careful urban planning policies or reconstruct historical scenarios (Farella et al., 2021).

Background: Stereotactic body radiotherapy (SBRT) targeted at metastatic sites of disease progression is emerging as a potential therapeutic approach for managing oligoprogressive prostate cancer. However, a definitive benefit has yet to be demonstrated. Herein, we present our institution’s experience with this treatment approach. Methods: From April 2018 to March 2023, 11 patients affected by oligoprogressive prostate cancer were treated with SBRT targeting the nodal or bone sites of progression while maintaining the ongoing systemic therapy. Three patients were undergoing single-agent ADT (Androgen Deprivation Therapy), while the remaining eight were receiving a subsequent line of systemic therapy. All patients were evaluated with a pre-treatment 68Ga-PSMA-11 or 18F-fluorocholine PET/CT, which demonstrated between one and five localizations of disease. All the active sites were treated with SBRT in one (15–24 Gy) or three (21–27 Gy) fractions, except for one patient, who was treated in five fractions (35 Gy). PSA serum levels were tested at baseline, one month after RT and at least every three months; all patients underwent a post-treatment 68Ga-PSMA-11 or 18F-fluorocholine PET/CT. The evaluated endpoints were PSA response, defined as a post-treatment decrease >50% from baseline measured within 6 months, time to next-line systemic treatment (NEST), local control (LC), biochemical progression-free survival (bPFS), radiological progression-free survival (rPFS) and freedom from polymetastatic progression (FPP). Results: Nineteen lesions were treated (seven nodal and twelve bone). At a median follow-up of 19 months (7–63), 9 of the 11 patients had a PSA response; all patients had local control of the treated metastases. A total of six patients switched to a next-line systemic treatment, with a median NEST of 13 months. Six patients had polymetastatic progression with an FPP median time of 19 months. No patients died during the follow-up period. The SBRT-related toxicity was negligible. Conclusions: Our data support the use of SBRT targeting the sites of oligoprogressive disease before moving to a subsequent line of systemic treatment in patients with metastatic prostate cancer. Prospective studies to evaluate the potential impact of this approach on overall survival are warranted.

A literature review was conducted using the key words relating to Native American Youth and 4-H to assess the current state of 4-H youth programming serving First Nation/ Indigenous populations to inform future Extension initiatives. A systematic and qualitative review determined what level of focus the conducted programming efforts placed on broadly accepted elements of cultural identity as noted in the Peoplehood Model. A very small number of articles (N=13) were found pertaining to 4-H and Indigenous Communities. Fewer demonstrated emphasis on the peoplehood elements of language, place, traditional ceremony or calendars, and history. This work investigates a continuing inequity in 4-H PYD--both in service and reporting--and suggests some next steps for creating a more inclusive 4-H program for Native American/First Nation/Indigenous youth.

In recent years, the scientific community’s interest in nanoscience and nanotechnology stems from the increasing capability to manipulate matter at the nanoscale. Nanotechnology development is closely linked to fabricating and characterizing structures below 100 nm, driven by technological advancements enabling their in-depth analysis. Up to now, several top-down and bottom-up nanofabrication approaches have been developed to realize a plethora of nanostructures. Although effective, these methods have many drawbacks like high costs and limitations in feature size. In this scenario, Scanning Probe-based Lithography (SPL) emerges as a very promising alternative to conventional nanofabrication techniques, overcoming their main method limitations with versatility, flexibility, low cost, and nanoscale resolution. This review focuses on mechanical Scanning Probe-based Lithography (m-SPL), tracing its evolution from inception to recent advances. Different m-SPL methods, such as Nanoindentation, Static and Dynamic Plowing lithography, Nanomilling, and their variants are discussed in-depth, emphasizing their advantages and drawbacks, and highlighting their application. Moreover, this review explores the effects of combining m-SPL with other energy sources, such as heat and electric energy, and outlines future perspectives in the field. Overall, m-SPL stands out as a promising avenue in nanofabrication, offering sub-nanometer resolution and diverse material manipulation capabilities.

In recent years, the cultural heritage (CH) sector has experienced a rapid evolution due to the introduction of increasingly powerful digital technologies and ICT (Information and Communication Technologies) solutions. As for many other domains, digital data, Artificial Intelligence (AI), and Extended Reality (XR) are opening up extraordinary opportunities for expanding heritage knowledge capabilities while boosting the research on innovative solutions for its valorisation and preservation. Being aware of the fundamental and strategic role of CH in the history and identity of the European countries, the European Commission has assumed a central role in fuelling the policy debate and putting together stakeholders to take a step forward in CH digitization and use, primarily through initiatives, programs, and recommendations. Within this framework, the ongoing European 5DCulture project (https://www.5dculture.eu/) has been funded to enrich the offer of 3D CH digital assets in the common European Data Space for Cultural Heritage by creating high-quality 3D contents and to foster their re-use in several sectors, from tourism to education. Through 8 re-use scenarios around historic buildings and cityscapes, archaeology, and fashion, the project aims to deliver a set of digital tools and increase the capacity of CH institutions to more effectively re-use their 3D digital assets.

Abstract Augmented Reality in education can support students in a wide range of cognitive tasks–fostering understanding, remembering, applying, analysing, evaluating, and creating learning-relevant information more easily. It can help keep up engagement, and it can render learning more fun. Within the framework of a multi-year investigation encompassing primary and secondary schools across Europe, the ARETE project developed several Augmented Reality applications, providing tools for user interaction and data collection in the education sector. The project developed innovative AR learning technology and methodology, validating these in four comprehensive pilot studies, in total involving more than 2,900 students and teachers. Each pilot made use of a different Augmented Reality application covering specific subjects (English literacy skills, Mathematics and Geography, Positive Behaviour, plus, additionally, an Augmented Reality authoring tool applied in a wide range of subjects). In this paper, we introduce the datasets collected during the pilots, describe how the data enabled the validation of the technology, and how the approach chosen could enhance existing augmented reality applications in data exploration and modelling.

In recent decades, the geospatial domain has benefitted from technological advances in sensors, methodologies, and processing tools to expand capabilities in mapping applications. Airborne techniques (LiDAR and aerial photogrammetry) generally provide most of the data used for this purpose. However, despite the relevant accuracy of these technologies and the high spatial resolution of airborne data, updates are not sufficiently regular due to significant flight costs and logistics. New possibilities to fill this information gap have emerged with the advent of Very High Resolution (VHR) optical satellite images in the early 2000s. In addition to the high temporal resolution of the cost-effective datasets and their sub-meter geometric resolutions, the synoptic coverage is an unprecedented opportunity for mapping remote areas, multi-temporal analyses, updating datasets and disaster management. For all these reasons, VHR satellite imagery is clearly a relevant study for National Mapping and Cadastral Agencies (NMCAs). This work, supported by EuroSDR, summarises a series of experimental analyses carried out over diverse landscapes to explore the potential of VHR imagery for large-scale mapping.

3D reconstruction from single and multi-view stereo images is still an open research topic, despite the high number of solutions proposed in the last decades. The surge of deep learning methods has then stimulated the development of new methods using monocular (MDE, Monocular Depth Estimation), stereoscopic and Multi-View Stereo (MVS) 3D reconstruction, showing promising results, often comparable to or even better than traditional methods. The more recent development of NeRF (Neural Radial Fields) has further triggered the interest for this kind of solution. Most of the proposed approaches, however, focus on terrestrial applications (e.g., autonomous driving or small artefacts 3D reconstructions), while airborne and UAV acquisitions are often overlooked. The recent introduction of new datasets, such as UseGeo has, therefore, given the opportunity to assess how state-of-the-art MDE, MVS and NeRF 3D reconstruction algorithms perform using airborne UAV images, allowing their comparison with LiDAR ground truth. This paper aims to present the results achieved by two MDE, two MVS and two NeRF approaches levering deep learning approaches, trained and tested using the UseGeo dataset. This work allows the comparison with a ground truth showing the current state of the art of these solutions and providing useful indications for their future development and improvement.

In recent years, the improvement of sensors and methodologies for 3D reality-based surveying has exponentially enhanced the possibility of creating digital replicas of the real world. LiDAR technologies and photogrammetry are currently standard approaches for collecting 3D geometric information of indoor and outdoor environments at different scales. This information can potentially be part of a broader processing workflow that, starting from 3D surveyed data and through Building Information Models (BIM) generation, leads to more complex analyses of buildings’ features and behavior (Figure 1). However, creating BIM models, especially of historic and heritage assets (HBIM), is still resource-intensive and time-consuming due to the manual efforts required for data creation and enrichment. Improve 3D data processing, interoperability, and the automation of the BIM generation process are some of the trending research topics, and benchmark datasets are extremely helpful in evaluating newly developed algorithms and methodologies for these scopes. This paper introduces the InCUBE dataset, resulting from the activities of the recently funded EU InCUBE project, focused on unlocking the EU building renovation through integrated strategies and processes for efficient built-environment management (including the use of innovative renewable energy technologies and digitalization). The set of data collects raw and processed data produced for the Italian demo site in the Santa Chiara district of Trento (Italy). The diversity of the shared data enables multiple possible uses, investigations and developments, and some of them are presented in this contribution.