SINGLE BUILDING POINT CLOUD SEGMENTATION: TOWARDS URBAN DATA MODELING AND MANAGEMENT

To manage urban areas, a key step is the development of a geometric survey and its subsequent analysis and processing in order to provide useful information, and to become a good basis for urban modeling. Surveys of urban areas can be developed with various technologies, such as Aerial Laser Scanning, Unmanned Aerial Systems photogrammetry, and Mobile Mapping Systems. To make the resulting point clouds useful for subsequent steps, it is necessary to segment them into classes representing urban elements. On the other hand, there are 2D land representations that provide a variety of information related to the elements in the urban environment, which are linked to databases that have information content related to them. In this context, the element identified as interesting for urban management of the built heritage is the individual building unit. This paper presents an automated method for using map datasets to segment individual building units on a point cloud of an urban area. A unique number is then assigned to the segmented points, linking them directly to the corresponding element in the map database. The resulting point cloud thus becomes a container of the information in the map database, and a basis for possible city modeling. The method was successfully tested on the historic city of Sabbioneta (northern Italy), using two point clouds, one obtained through the use of a Mobile Mapping System and one obtained with Unmanned Aerial System photogrammetry. Two cartographic databases were used, one opensource (OpenStreetMap) and one provided by the regional authorities (regional cartographic database)

Accessible path finding for historic urban environments: feature extraction and vectorization from point clouds

Sidewalk inventory is a topic whose importance is increasing together with the widespread use of smart city management. In order to manage the city properly and to make informed decisions, it is necessary to know the real conditions of the city. Furthermore, when planning and calculating cultural routes within the city, these routes must take into account the specific needs of all users. Therefore, it is important to know the conditions of the city’s sidewalk network and also their physical and geometrical characteristics. Typically, sidewalk network are generated basing on existing cartographic data, and sidewalk attributes are gathered through crowdsourcing. In this paper, the sidewalk network of an historic city was produced starting from point cloud data. The point cloud was semantically segmented in ”roads” and ”sidewalks”, and then the cluster of points of sidewalks surfaces were used to compute sidewalk attributes and to generate a vector layer composed of nodes and edges. The vector layer was then used to compute accessible paths between Points of Interest, using QGIS. The tests made on a real case study, the historic city and UNESCO site of Sabbioneta (Italy), shows a vectorization accuracy of 98.7%. In future, the vector layers and the computed paths could be used to generate maps for city planners, and to develop web or mobile phones routing apps.Ministerio de Ciencia e Innovación | Ref. RYC2020-029193-

HBIM STRUCTURAL MODEL TO EVALUATE BUILDING EVOLUTION AND CONSTRUCTION HYPOTHESES: PRELIMINARY RESULTS

Historic Building Information Modelling (HBIM) is a technology that has proven to be very effective for the management, preservation, and maintenance of heritage buildings. HBIM allows a digital replica of the building, in which information can be stored, designs can be made, and future actions can be planned. To do this, it is obviously necessary to have a thorough knowledge of the building and its historical evolution. The HBIM model can therefore become the ideal place in which to develop and model construction hypotheses of building portions that no longer exist, or even record its development over time using different phases of work. Based on this context, the aim of this article is to use the HBIM approach for modelling different construction hypotheses and use the model to study the behaviour of different configurations with structural analysis. To do this, the case study of the church of San Michele Maggiore in Pavia was chosen, which in the 15th century underwent major restorations due to structural failures of the vaults of the central nave, which were replaced with the current cross vaults. In the literature there are different constructive hypotheses of the ancient vaults, which have been modelled in HBIM precisely to evaluate the different structural behaviours following the method presented. This article presents the historical analyses and geometric surveys that led to the HBIM modelling and the model itself. In the future, after careful selection of the most appropriate software, structural calculations will be made to study the structural behaviour of the building

Spatial coding of word-initial letters: Evidence from a Simon-like task

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A DEEP LEARNING APPROACH FOR THE RECOGNITION OF URBAN GROUND PAVEMENTS IN HISTORICAL SITES

Urban management is a topic of great interest for local administrators, particularly because it is strongly connected to smart city issues and can have a great impact on making cities more sustainable. In particular, thinking about the management of the physical accessibility of cities, the possibility of automating data collection in urban areas is of great interest. Focusing then on historical centres and urban areas of cities and historical sites, it can be noted that their ground surfaces are generally characterised by the use of a multitude of different pavements. To strengthen the management of such urban areas, a comprehensive mapping of the different pavements can be very useful. In this paper, the survey of a historical city (Sabbioneta, in northern Italy) carried out with a Mobile Mapping System (MMS) was used as a starting point. The approach here presented exploit Deep Learning (DL) to classify the different pavings. Firstly, the points belonging to the ground surfaces of the point cloud were selected and the point cloud was rasterised. Then the raster images were used to perform a material classification using the Deep Learning approach, implementing U-Net coupled with ResNet 18. Five different classes of materials were identified, namely sampietrini, bricks, cobblestone, stone, asphalt. The average accuracy of the result is 94%

A deep learning approach for the recognition of urban ground pavements in historical sites

Urban management is a topic of great interest for local administrators, particularly because it is strongly connected to smart city issues and can have a great impact on making cities more sustainable. In particular, thinking about the management of the physical accessibility of cities, the possibility of automating data collection in urban areas is of great interest. Focusing then on historical centres and urban areas of cities and historical sites, it can be noted that their ground surfaces are generally characterised by the use of a multitude of different pavements. To strengthen the management of such urban areas, a comprehensive mapping of the different pavements can be very useful. In this paper, the survey of a historical city (Sabbioneta, in northern Italy) carried out with a Mobile Mapping System (MMS) was used as a starting point. The approach here presented exploit Deep Learning (DL) to classify the different pavings. Firstly, the points belonging to the ground surfaces of the point cloud were selected and the point cloud was rasterised. Then the raster images were used to perform a material classification using the Deep Learning approach, implementing U-Net coupled with ResNet 18. Five different classes of materials were identified, namely sampietrini, bricks, cobblestone, stone, asphalt. The average accuracy of the result is 94%.Xunta de Galicia | Ref. ED481B-2019-061Xunta de Galicia | Ref. ED431C 2020/01Ministerio de Ciencia e Innovación | Ref. PID2019-105221RB-C43Ministerio de Ciencia e Innovación | Ref. RYC2020-029193-

HIGH-RESOLUTION DIGITAL SURVEY OF FLOORS: A NEW PROTOTYPE FOR EFFICIENT PHOTOGRAMMETRIC ACQUISITION

High-resolution surveying of historical floors is a very common practice in both research and everyday life. The type of floors typically concerned are made of mosaic, marble and stone. Because of their intrinsic characteristics, their survey typically requires very highresolution results, to ensure excellent support for restoration, as well as in-depth knowledge of the artifact. In these cases, the focus must be kept on both geometric and radiometric content, to enable accurate metric representation and a rendering of colour and surfaces as close as possible to reality. In this research we propose a prototype of a photogrammetric acquisition system (under development) which tries to optimise the floor survey in terms of both geometric and colour documentation. In particular, the prototype makes use of the cross-polarisation technique with the aim of eliminating reflections from the images. The principle behind the prototype is the creation of a movable laboratory, a segregated space that allows excellent photographic acquisition even in difficult environmental conditions, which cannot always be controlled optimally. First tests showed its suitability and usefulness to reach the goal of a high resolution survey of historic floors

GEOMETRIC SURVEY DATA AND HISTORICAL SOURCES INTERPRETATION FOR HBIM PROCESS: THE CASE OF MANTUA CATHEDRAL FAÇADE

Planned conservation approach requires a sustained, long-term action to better manage the cultural heritage assets during their life cycle. Together with programmed conservation and local interventions, there is a large amount of information related to the building; it emerges the need for an appropriate tool in which to store all data. Historic Building Information Modelling (HBIM) can be an appropriate way to address this issue. In this context, the lack of automatic tools (to speed up the project) and the need for data interpretation in the process are noticeable, especially for cultural heritage items. In this paper we present a practical case study. Starting from an integrated survey of Mantua Cathedral (located in Northern Italy) we developed a HBIM model of its façade. Particular emphasis is given to data interpretation both from geometrical survey and from historical sources. The resulting model is consistent and coherent with reality. As a result, we state that the development of a HBIM model is not an automatized process. In the process, from the survey to the final model, there is the need for a deep knowledge and a deep understanding of the building, not only in term of geometrical survey but also of its historical phases, its changes in time, its materials and the construction techniques. HBIM can be a useful instrument for planned conservation, which strongly requires a coherent model to be effective and useful. A proper model, working as an integrated archive, can increase the effectiveness of planned conservation

Higher-order QED corrections to W-boson mass determination at hadron colliders

The impact of higher-order final-state photonic corrections on the precise determination of the W-boson mass at the Tevatron and LHC colliders is evaluated. In the presence of realistic selection criteria, the shift in the W mass from a fit to the transverse mass distribution is found to be about 10 MeV in the $W \to \mu \nu$ channel and almost negligible in the $W \to e \nu$ channel. The calculation, which is implemented in a Monte Carlo event generator for data analysis, can contribute to reduce the uncertainty associated to the W mass measurement at future hadron collider experiments.Comment: 9 pages, 2 figures, 1 table, RevTe

Modelling systemic price cojumps with Hawkes factor models

Instabilities in the price dynamics of a large number of financial assets are a clear sign of systemic events. By investigating portfolios of highly liquid stocks, we find that there are a large number of high-frequency cojumps. We show that the dynamics of these jumps is described neither by a multivariate Poisson nor by a multivariate Hawkes model. We introduce a Hawkes one-factor model which is able to capture simultaneously the time clustering of jumps and the high synchronization of jumps across assets