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-

Digital, memory and mixed-signal test engineering education: five centres of competence in Europe

The launching of the EuNICE-Test project was announced two years ago at the first DELTA Conference. This project is now completed and the present paper describes the project actions and outcomes. The original idea was to build a long-lasting European Network for test engineering education using both test resource mutualisation and remote experiments. This objective is fully fulfilled and we have now, in Europe, five centres of competence able to deliver high-level and high-specialized training courses in the field of test engineering using a high-performing industrial ATE. All the centres propose training courses on digital testing, three of them propose mixed-signal trainings and three of them propose memory trainings. Taking into account the demand in test engineering, the network is planned to continue in a stand alone mode after project end. Nevertheless a new European proposal with several new partners and new test lessons is under construction

ROBUST TECHNIQUES FOR BUILDING FOOTPRINT EXTRACTION IN AERIAL LASER SCANNING 3D POINT CLOUDS

The building footprint is crucial for a volumetric 3D representation of a building that is applied in urban planning, 3D city modeling, cadastral and topographic map generation. Aerial laser scanning (ALS) has been recognized as the most suitable means of large-scale 3D point cloud data (PCD) acquisition. PCD can produce geometric detail of a scanned surface. However, it is almost impossible to get point clouds without noise and outliers. Besides, data incompleteness and occlusions are two common phenomena for PCD. Most of the existing methods for building footprint extraction employ classification, segmentation, voting techniques (e.g., Hough-Transform or RANSAC), or Principal Component Analysis (PCA) based methods. It is known that classical PCA is highly sensitive to outliers, even RANSAC which is known as a robust technique for shape detection is not free from outlier effects. This paper presents a novel algorithm that employs MCMD (maximum consistency within minimum distance), MSAC (a robust variant of RANSAC) and a robust regression to extract reliable building footprints in the presence of outliers, missing points and irregular data distributions. The algorithm is successfully demonstrated through two sets of ALS PCD

Test engineering education in Europe: the EuNICE-Test project

The paper deals with a European experience of education in industrial test of ICs and SoCs using remote testing facilities. The project addresses the problem of the shortage in microelectronics engineers aware with the new challenge of testing mixed-signal SoCs far multimedia/telecom market. It aims at providing test training facilities at a European scale in both initial and continuing education contexts. This is done by allowing the academic and industrial partners of the consortium to train engineers using the common test resources center (CRTC) hosted by LIRMM (Laboratoire d'Informatique, de Robotique et de Microelectronique de Montpellier, France). CRTC test tools include up-to-date/high-tech testers that are fully representative of real industrial testers as used on production testfloors. At the end of the project, it is aimed at reaching a cruising speed of about 16 trainees per year per center. Each trainee will have attend at least one one-week training using the remote test facilities of CRTC

TREE DIGITISATION FROM POINT CLOUDS WITH UNREAL ENGINE

Trees are fundamental parts of urban areas and green urbanism. Although much effort is being put into the digitisation of urban areas, trees present great complexity and are usually replaced by predefined models. On the one hand, trees are elements composed of trunk, branches, and leaves, each with a completely different structure and geometry. On the other hand, the tree parts are closely related to each species. Therefore, in order to obtain a realistic digital urban environment, in 3D models such as CityGML or Metaverse, it is necessary that the trees correspond faithfully to reality. The aim of this work is to propose a method to digitise trees from Mobile Laser Scanning and Terrestrial Laser Scanning data. The process takes advantage of the differentiation between trunks and leaves for their segmentation by point cloud geometric features. Unreal Engine is then used to digitise each part. Trunk and branches are geometrically preserved. For dense canopy trees, predefined leaves according to the species are imported and the alpha shape of the crown is filled. For non-dense canopy trees, the canopy is imported and modified to fit the branches. The method was tested on four real case studies. The results show realistic trees, with correct trunk and foliage segmentation, but highly dependent on the life/canopy repositories. Unreal Engine was a very complete and useful tool for the digitisation of trees generating realistic textures and lighting options

El precio del agua en el sector turístico: claves para la sostenibilidad

La Agenda de Desarrollo Sostenible de las Naciones Unidas (UN, 2015), recoge en su objetivo número 6 la necesidad de garantizar la disponibilidad y saneamiento del agua, así como su gestión sostenible y eficiente. La sostenibilidad en el uso del agua es necesaria, especialmente en áreas con importante estrés hídrico, tal como es el caso de España. Las actividades turísticas son parte de ese complejo entramado de usuarios del agua que ejercen presiones sobre el recurso [1], y que, por lo tanto, requieren de políticas específicas que incentiven un consumo racional del mismo

Enhancing Reinforced Concrete Bridge Health Monitoring: A Case Study on the Integration of InSAR, GPR, and LiDAR within 3D GIS Environment

This work presents a preliminary case study on assessing reinforced concrete pedestrian bridge conditions in A Coruña (Galicia, Spain), employing a novel integration of non-destructive testing (NDT) technologies. The research aims to refine the health monitoring process of pedestrian bridges by adopting a top-down approach, leveraging the data-fusion concept to enhance the analysis of structural defects. A data-fusion methodology that integrates interferometric synthetic aperture radar (InSAR), ground penetrating radar (GPR), and light detection and range (LiDAR) technologies is introduced. Then, the data are visualized within a 3D Geographical Information System (GIS) environment. The MT-InSAR technique is used at the network level to identify bridges requiring detailed inspections. Subsequently, selected bridges undergo thorough examinations using GPR and LiDAR technologies from fieldwork between 2021 and 2023. A comparative analysis of three different LiDAR devices and two GPR setups is conducted to evaluate their effectiveness in capturing detailed structural data. The study also explores the integration challenges and solutions for combining diverse data formats and the possibility of using advanced digital technologies, such as Building Information Modelling (BIM), to facilitate a seamless transition from traditional NDT approaches to a digitized, model-based inspection framework. The findings highlight the advantages of each NDT method, address specific data acquisition challenges, and propose strategies for overcoming issues related to data integration, visualization, and the accuracy of spatial localization. Integrating these NDT technologies within a georeferenced 3D GIS environment facilitates a detailed understanding of the bridge's condition and enhances decision-making processes for maintenance and rehabilitation efforts

Exoplanet atmospheres Characterization Observatory payload short-wave infrared channel: EChO SWiR

EChO (Exoplanet atmospheres Characterization Observatory), a proposal for exoplanets exploration space mission, is considered the next step for planetary atmospheres characterization. It would be a dedicated observatory to uncover a large selected sample of planets spanning a wide range of masses (from gas giants to super-Earths) and orbital temperatures (from hot to habitable). All targets move around stars of spectral types F, G, K, and M. EChO would provide an unprecedented view of the atmospheres of planets in the solar neighbourhood. The consortium formed by various institutions of different countries proposed as ESA M3 an integrated spectrometer payload for EChO covering the wavelength interval 0.4 to 16 µm. This instrument is subdivided into 4 channels: a visible channel, which includes a fine guidance system (FGS) and a VIS spectrometer, a near infrared channel (SWiR), a middle infrared channel (MWiR), and a long wave infrared module (LWiR). In addition, it contains a common set of optics spectrally dividing the wavelength coverage and injecting the combined light of parent stars and their exoplanets into the different channels. The proposed payload meets all of the key performance requirements detailed in the ESA call for proposals as well as all scientific goals. EChO payload is based on different spectrometers covering the spectral range mentioned above. Among them, SWiR spectrometer would work from 2.45 microns to 5.45 microns. In this paper, the optical and mechanical designs of the SWiR channel instrument are reported on