A window to the past through modern urban environments: Developing a photogrammetric workflow for the orientation parameter estimation of historical images

The ongoing process of digitization in archives is providing access to ever-increasing historical image collections. In many of these repositories, images can typically be viewed in a list or gallery view. Due to the growing number of digitized objects, this type of visualization is becoming increasingly complex. Among other things, it is difficult to determine how many photographs show a particular object and spatial information can only be communicated via metadata. Within the scope of this thesis, research is conducted on the automated determination and provision of this spatial data. Enhanced visualization options make this information more eas- ily accessible to scientists as well as citizens. Different types of visualizations can be presented in three-dimensional (3D), Virtual Reality (VR) or Augmented Reality (AR) applications. However, applications of this type require the estimation of the photographer’s point of view. In the photogrammetric context, this is referred to as estimating the interior and exterior orientation parameters of the camera. For determination of orientation parameters for single images, there are the established methods of Direct Linear Transformation (DLT) or photogrammetric space resection. Using these methods requires the assignment of measured object points to their homologue image points. This is feasible for single images, but quickly becomes impractical due to the large amount of images available in archives. Thus, for larger image collections, usually the Structure-from-Motion (SfM) method is chosen, which allows the simultaneous estimation of the interior as well as the exterior orientation of the cameras. While this method yields good results especially for sequential, contemporary image data, its application to unsorted historical photographs poses a major challenge. In the context of this work, which is mainly limited to scenarios of urban terrestrial photographs, the reasons for failure of the SfM process are identified. In contrast to sequential image collections, pairs of images from different points in time or from varying viewpoints show huge differences in terms of scene representation such as deviations in the lighting situation, building state, or seasonal changes. Since homologue image points have to be found automatically in image pairs or image sequences in the feature matching procedure of SfM, these image differences pose the most complex problem. In order to test different feature matching methods, it is necessary to use a pre-oriented historical dataset. Since such a benchmark dataset did not exist yet, eight historical image triples (corresponding to 24 image pairs) are oriented in this work by manual selection of homologue image points. This dataset allows the evaluation of frequently new published methods in feature matching. The initial methods used, which are based on algorithmic procedures for feature matching (e.g., Scale Invariant Feature Transform (SIFT)), provide satisfactory results for only few of the image pairs in this dataset. By introducing methods that use neural networks for feature detection and feature description, homologue features can be reliably found for a large fraction of image pairs in the benchmark dataset. In addition to a successful feature matching strategy, determining camera orientation requires an initial estimate of the principal distance. Hence for historical images, the principal distance cannot be directly determined as the camera information is usually lost during the process of digitizing the analog original. A possible solution to this problem is to use three vanishing points that are automatically detected in the historical image and from which the principal distance can then be determined. The combination of principal distance estimation and robust feature matching is integrated into the SfM process and allows the determination of the interior and exterior camera orientation parameters of historical images. Based on these results, a workflow is designed that allows archives to be directly connected to 3D applications. A search query in archives is usually performed using keywords, which have to be assigned to the corresponding object as metadata. Therefore, a keyword search for a specific building also results in hits on drawings, paintings, events, interior or detailed views directly connected to this building. However, for the successful application of SfM in an urban context, primarily the photographic exterior view of the building is of interest. While the images for a single building can be sorted by hand, this process is too time-consuming for multiple buildings. Therefore, in collaboration with the Competence Center for Scalable Data Services and Solutions (ScaDS), an approach is developed to filter historical photographs by image similarities. This method reliably enables the search for content-similar views via the selection of one or more query images. By linking this content-based image retrieval with the SfM approach, automatic determination of camera parameters for a large number of historical photographs is possible. The developed method represents a significant improvement over commercial and open-source SfM standard solutions. The result of this work is a complete workflow from archive to application that automatically filters images and calculates the camera parameters. The expected accuracy of a few meters for the camera position is sufficient for the presented applications in this work, but offer further potential for improvement. A connection to archives, which will automatically exchange photographs and positions via interfaces, is currently under development. This makes it possible to retrieve interior and exterior orientation parameters directly from historical photography as metadata which opens up new fields of research.:1 Introduction 1 1.1 Thesis structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Historical image data and archives . . . . . . . . . . . . . . . . . . . . . . . . 2 1.3 Structure-from-Motion for historical images . . . . . . . . . . . . . . . . . . . 4 1.3.1 Terminology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 1.3.2 Selection of images and preprocessing . . . . . . . . . . . . . . . . . . 5 1.3.3 Feature detection, feature description and feature matching . . . . . . 6 1.3.3.1 Feature detection . . . . . . . . . . . . . . . . . . . . . . . . 7 1.3.3.2 Feature description . . . . . . . . . . . . . . . . . . . . . . . 9 1.3.3.3 Feature matching . . . . . . . . . . . . . . . . . . . . . . . . 10 1.3.3.4 Geometric verification and robust estimators . . . . . . . . . 13 1.3.3.5 Joint methods . . . . . . . . . . . . . . . . . . . . . . . . . . 16 1.3.4 Initial parameterization . . . . . . . . . . . . . . . . . . . . . . . . . . 19 1.3.5 Bundle adjustment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 1.3.6 Dense reconstruction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 1.3.7 Georeferencing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 1.4 Research objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 2 Generation of a benchmark dataset using historical photographs for the evaluation of feature matching methods 29 2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 2.1.1 Image differences based on digitization and image medium . . . . . . . 30 2.1.2 Image differences based on different cameras and acquisition technique 31 2.1.3 Object differences based on different dates of acquisition . . . . . . . . 31 2.2 Related work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 2.3 The image dataset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 2.4 Comparison of different feature detection and description methods . . . . . . 35 2.4.1 Oriented FAST and Rotated BRIEF (ORB) . . . . . . . . . . . . . . . 36 2.4.2 Maximally Stable Extremal Region Detector (MSER) . . . . . . . . . 36 2.4.3 Radiation-invariant Feature Transform (RIFT) . . . . . . . . . . . . . 36 2.4.4 Feature matching and outlier removal . . . . . . . . . . . . . . . . . . 36 2.5 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37 2.6 Conclusions and future work . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40 3 Photogrammetry as a link between image repository and 4D applications 45 3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46 IX Contents 3.2 Multimodal access on repositories . . . . . . . . . . . . . . . . . . . . . . . . . 47 3.2.1 Conventional access . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 3.2.2 Virtual access using online collections . . . . . . . . . . . . . . . . . . 48 3.2.3 Virtual museums . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50 3.3 Workflow and access strategies . . . . . . . . . . . . . . . . . . . . . . . . . . 52 3.3.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52 3.3.2 Filtering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53 3.3.3 Photogrammetry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54 3.3.4 Browser access . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58 3.3.5 VR and AR access . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61 3.4 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64 4 An adapted Structure-from-Motion Workflow for the orientation of historical images 69 4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70 4.2 Related Research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72 4.2.1 Historical images for 3D reconstruction . . . . . . . . . . . . . . . . . 72 4.2.2 Algorithmic Feature Detection and Matching . . . . . . . . . . . . . . 73 4.2.3 Feature Detection and Matching using Convolutional Neural Networks 74 4.3 Feature Matching . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75 4.4 Workflow . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77 4.4.1 Step 1: Data preparation . . . . . . . . . . . . . . . . . . . . . . . . . 78 4.4.2 Step 2.1: Feature Detection and Matching . . . . . . . . . . . . . . . . 78 4.4.3 Step 2.2: Vanishing Point Detection and Principal Distance Estimation 80 4.4.4 Step 3: Scene Reconstruction . . . . . . . . . . . . . . . . . . . . . . . 80 4.4.5 Comparison with Three Other State-of-the-Art SfM Workflows . . . . 81 4.5 Datasets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81 4.6 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83 4.7 Conclusions and Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . 85 4.8 Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86 4.A Appendix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90 5 Fully automated pose estimation of historical images 97 5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98 5.2 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100 5.2.1 Image Retrieval . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100 5.2.2 Feature Detection and Matching . . . . . . . . . . . . . . . . . . . . . 101 5.3 Data Preparation: Image Retrieval . . . . . . . . . . . . . . . . . . . . . . . . 102 5.3.1 Experiment and Data . . . . . . . . . . . . . . . . . . . . . . . . . . . 103 5.3.2 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104 5.3.2.1 Layer Extraction Approach (LEA) . . . . . . . . . . . . . . . 104 5.3.2.2 Attentive Deep Local Features (DELF) Approach . . . . . . 105 5.3.3 Results and Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . 106 5.4 Camera Pose Estimation of Historical Images Using Photogrammetric Methods 110 5.4.1 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 110 5.4.1.1 Benchmark Datasets . . . . . . . . . . . . . . . . . . . . . . . 111 5.4.1.2 Retrieval Datasets . . . . . . . . . . . . . . . . . . . . . . . . 113 5.4.2 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115 5.4.2.1 Feature Detection and Matching . . . . . . . . . . . . . . . . 115 5.4.2.2 Geometric Verification and Camera Pose Estimation . . . . . 116 5.4.3 Results and Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . 117 5.5 Conclusions and Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . 120 5.A Appendix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 124 6 Related publications 129 6.1 Photogrammetric analysis of historical image repositores for virtual reconstruction in the field of digital humanities . . . . . . . . . . . . . . . . . . . . . . . 130 6.2 Feature matching of historical images based on geometry of quadrilaterals . . 131 6.3 Geo-information technologies for a multimodal access on historical photographs and maps for research and communication in urban history . . . . . . . . . . 132 6.4 An automated pipeline for a browser-based, city-scale mobile 4D VR application based on historical images . . . . . . . . . . . . . . . . . . . . . . . . . . 133 6.5 Software and content design of a browser-based mobile 4D VR application to explore historical city architecture . . . . . . . . . . . . . . . . . . . . . . . . 134 7 Synthesis 135 7.1 Summary of the developed workflows . . . . . . . . . . . . . . . . . . . . . . . 135 7.1.1 Error assessment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137 7.1.2 Accuracy estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 139 7.1.3 Transfer of the workflow . . . . . . . . . . . . . . . . . . . . . . . . . . 141 7.2 Developments and Outlook . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145 8 Appendix 149 8.1 Setup for the feature matching evaluation . . . . . . . . . . . . . . . . . . . . 149 8.2 Transformation from COLMAP coordinate system to OpenGL . . . . . . . . 150 References 151 List of Figures 165 List of Tables 167 List of Abbreviations 169Der andauernde Prozess der Digitalisierung in Archiven ermöglicht den Zugriff auf immer größer werdende historische Bildbestände. In vielen Repositorien können die Bilder typischerweise in einer Listen- oder Gallerieansicht betrachtet werden. Aufgrund der steigenden Zahl an digitalisierten Objekten wird diese Art der Visualisierung zunehmend unübersichtlicher. Es kann u.a. nur noch schwierig bestimmt werden, wie viele Fotografien ein bestimmtes Motiv zeigen. Des Weiteren können räumliche Informationen bisher nur über Metadaten vermittelt werden. Im Rahmen der Arbeit wird an der automatisierten Ermittlung und Bereitstellung dieser räumlichen Daten geforscht. Erweiterte Visualisierungsmöglichkeiten machen diese Informationen Wissenschaftlern sowie Bürgern einfacher zugänglich. Diese Visualisierungen können u.a. in drei-dimensionalen (3D), Virtual Reality (VR) oder Augmented Reality (AR) Anwendungen präsentiert werden. Allerdings erfordern Anwendungen dieser Art die Schätzung des Standpunktes des Fotografen. Im photogrammetrischen Kontext spricht man dabei von der Schätzung der inneren und äußeren Orientierungsparameter der Kamera. Zur Bestimmung der Orientierungsparameter für Einzelbilder existieren die etablierten Verfahren der direkten linearen Transformation oder des photogrammetrischen Rückwärtsschnittes. Dazu muss eine Zuordnung von gemessenen Objektpunkten zu ihren homologen Bildpunkten erfolgen. Das ist für einzelne Bilder realisierbar, wird aber aufgrund der großen Menge an Bildern in Archiven schnell nicht mehr praktikabel. Für größere Bildverbände wird im photogrammetrischen Kontext somit üblicherweise das Verfahren Structure-from-Motion (SfM) gewählt, das die simultane Schätzung der inneren sowie der äußeren Orientierung der Kameras ermöglicht. Während diese Methode vor allem für sequenzielle, gegenwärtige Bildverbände gute Ergebnisse liefert, stellt die Anwendung auf unsortierten historischen Fotografien eine große Herausforderung dar. Im Rahmen der Arbeit, die sich größtenteils auf Szenarien stadträumlicher terrestrischer Fotografien beschränkt, werden zuerst die Gründe für das Scheitern des SfM Prozesses identifiziert. Im Gegensatz zu sequenziellen Bildverbänden zeigen Bildpaare aus unterschiedlichen zeitlichen Epochen oder von unterschiedlichen Standpunkten enorme Differenzen hinsichtlich der Szenendarstellung. Dies können u.a. Unterschiede in der Beleuchtungssituation, des Aufnahmezeitpunktes oder Schäden am originalen analogen Medium sein. Da für die Merkmalszuordnung in SfM automatisiert homologe Bildpunkte in Bildpaaren bzw. Bildsequenzen gefunden werden müssen, stellen diese Bilddifferenzen die größte Schwierigkeit dar. Um verschiedene Verfahren der Merkmalszuordnung testen zu können, ist es notwendig einen vororientierten historischen Datensatz zu verwenden. Da solch ein Benchmark-Datensatz noch nicht existierte, werden im Rahmen der Arbeit durch manuelle Selektion homologer Bildpunkte acht historische Bildtripel (entspricht 24 Bildpaaren) orientiert, die anschließend genutzt werden, um neu publizierte Verfahren bei der Merkmalszuordnung zu evaluieren. Die ersten verwendeten Methoden, die algorithmische Verfahren zur Merkmalszuordnung nutzen (z.B. Scale Invariant Feature Transform (SIFT)), liefern nur für wenige Bildpaare des Datensatzes zufriedenstellende Ergebnisse. Erst durch die Verwendung von Verfahren, die neuronale Netze zur Merkmalsdetektion und Merkmalsbeschreibung einsetzen, können für einen großen Teil der historischen Bilder des Benchmark-Datensatzes zuverlässig homologe Bildpunkte gefunden werden. Die Bestimmung der Kameraorientierung erfordert zusätzlich zur Merkmalszuordnung eine initiale Schätzung der Kamerakonstante, die jedoch im Zuge der Digitalisierung des analogen Bildes nicht mehr direkt zu ermitteln ist. Eine mögliche Lösung dieses Problems ist die Verwendung von drei Fluchtpunkten, die automatisiert im historischen Bild detektiert werden und aus denen dann die Kamerakonstante bestimmt werden kann. Die Kombination aus Schätzung der Kamerakonstante und robuster Merkmalszuordnung wird in den SfM Prozess integriert und erlaubt die Bestimmung der Kameraorientierung historischer Bilder. Auf Grundlage dieser Ergebnisse wird ein Arbeitsablauf konzipiert, der es ermöglicht, Archive mittels dieses photogrammetrischen Verfahrens direkt an 3D-Anwendungen anzubinden. Eine Suchanfrage in Archiven erfolgt üblicherweise über Schlagworte, die dann als Metadaten dem entsprechenden Objekt zugeordnet sein müssen. Eine Suche nach einem bestimmten Gebäude generiert deshalb u.a. Treffer zu Zeichnungen, Gemälden, Veranstaltungen, Innen- oder Detailansichten. Für die erfolgreiche Anwendung von SfM im stadträumlichen Kontext interessiert jedoch v.a. die fotografische Außenansicht des Gebäudes. Während die Bilder für ein einzelnes Gebäude von Hand sortiert werden können, ist dieser Prozess für mehrere Gebäude zu zeitaufwendig. Daher wird in Zusammenarbeit mit dem Competence Center for Scalable Data Services and Solutions (ScaDS) ein Ansatz entwickelt, um historische Fotografien über Bildähnlichkeiten zu filtern. Dieser ermöglicht zuverlässig über die Auswahl eines oder mehrerer Suchbilder die Suche nach inhaltsähnlichen Ansichten. Durch die Verknüpfung der inhaltsbasierten Suche mit dem SfM Ansatz ist es möglich, automatisiert für eine große Anzahl historischer Fotografien die Kameraparameter zu bestimmen. Das entwickelte Verfahren stellt eine deutliche Verbesserung im Vergleich zu kommerziellen und open-source SfM Standardlösungen dar. Das Ergebnis dieser Arbeit ist ein kompletter Arbeitsablauf vom Archiv bis zur Applikation, der automatisch Bilder filtert und diese orientiert. Die zu erwartende Genauigkeit von wenigen Metern für die Kameraposition sind ausreichend für die dargestellten Anwendungen in dieser Arbeit, bieten aber weiteres Verbesserungspotential. Eine Anbindung an Archive, die über Schnittstellen automatisch Fotografien und Positionen austauschen soll, befindet sich bereits in der Entwicklung. Dadurch ist es möglich, innere und äußere Orientierungsparameter direkt von der historischen Fotografie als Metadaten abzurufen, was neue Forschungsfelder eröffnet.:1 Introduction 1 1.1 Thesis structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Historical image data and archives . . . . . . . . . . . . . . . . . . . . . . . . 2 1.3 Structure-from-Motion for historical images . . . . . . . . . . . . . . . . . . . 4 1.3.1 Terminology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 1.3.2 Selection of images and preprocessing . . . . . . . . . . . . . . . . . . 5 1.3.3 Feature detection, feature description and feature matching . . . . . . 6 1.3.3.1 Feature detection . . . . . . . . . . . . . . . . . . . . . . . . 7 1.3.3.2 Feature description . . . . . . . . . . . . . . . . . . . . . . . 9 1.3.3.3 Feature matching . . . . . . . . . . . . . . . . . . . . . . . . 10 1.3.3.4 Geometric verification and robust estimators . . . . . . . . . 13 1.3.3.5 Joint methods . . . . . . . . . . . . . . . .

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This volume is a product of the 13th International Conference on Archaeological Prospection 2019, which was hosted by the Department of Environmental Science in the Faculty of Science at the Institute of Technology Sligo. The conference is held every two years under the banner of the International Society for Archaeological Prospection and this was the first time that the conference was held in Ireland. New Global Perspectives on Archaeological Prospection draws together over 90 papers addressing archaeological prospection techniques, methodologies and case studies from 33 countries across Africa, Asia, Australasia, Europe and North America, reflecting current and global trends in archaeological prospection. At this particular ICAP meeting, specific consideration was given to the development and use of archaeological prospection in Ireland, archaeological feedback for the prospector, applications of prospection technology in the urban environment and the use of legacy data. Papers include novel research areas such as magnetometry near the equator, drone-mounted radar, microgravity assessment of tombs, marine electrical resistivity tomography, convolutional neural networks, data processing, automated interpretive workflows and modelling as well as recent improvements in remote sensing, multispectral imaging and visualisation

Towards an integrated vulnerability-based approach for evaluating, managing and mitigating earthquake risk in urban areas

Tese de doutoramento em Civil EngineeringSismos de grande intensidade, como aqueles que ocorreram na Turquía-Síria (2023) ou México (2017) deviam chamar a atenção para o projeto e implementação de ações proativas que conduzam à identificação de bens vulneráveis. A presente tese propõe um fluxo de trabalho relativamente simples para efetuar avaliações da vulnerabilidade sísmica à escala urbana mediante ferramentas digitais. Um modelo de vulnerabilidade baseado em parâmetros é adotado devido à afinidade que possui com o Catálogo Nacional de Monumentos Históricos mexicano. Uma primeira implementação do método (a grande escala) foi efetuada na cidade histórica de Atlixco (Puebla, México), demonstrando a sua aplicabilidade e algumas limitações, o que permitiu o desenvolvimento de uma estratégia para quantificar e considerar as incertezas epistémicas encontradas nos processos de aquisição de dados. Devido ao volume de dados tratado, foi preciso desenvolver meios robustos para obter, armazenar e gerir informações. O uso de Sistemas de Informação Geográfica, com programas à medida baseados em linguagem Python e a distribuição de ficheiros na ”nuvem”, facilitou a criação de bases de dados de escala urbana para facilitar a aquisição de dados em campo, os cálculos de vulnerabilidade e dano e, finalmente, a representação dos resultados. Este desenvolvimento foi a base para um segundo conjunto de trabalhos em municípios do estado de Morelos (México). A caracterização da vulnerabilidade sísmica de mais de 160 construções permitiu a avaliação da representatividade do método paramétrico pela comparação entre os níveis de dano teórico e os danos observados depois do terramoto de Puebla-Morelos (2017). Esta comparação foi a base para efetuar processos de calibração e ajuste assistidos por algoritmos de aprendizagem de máquina (Machine Learning), fornecendo bases para o desenvolvimento de modelos de vulnerabilidade à medida (mediante o uso de Inteligência Artificial), apoiados nas evidências de eventos sísmicos prévios.Strong seismic events like the ones of Türkiye-Syria (2023) or Mexico (2017) should guide our attention to the design and implementation of proactive actions aimed to identify vulnerable assets. This work is aimed to propose a suitable and easy-to-implement workflow for performing large-scale seismic vulnerability assessments in historic environments by means of digital tools. A vulnerability-oriented model based on parameters is adopted given its affinity with the Mexican Catalogue of Historical Monuments. A first large-scale implementation of this method in the historical city of Atlixco (Puebla, Mexico) demonstrated its suitability and some limitations, which lead to develop a strategy for quantifying and involving the epistemic uncertainties found during the data acquisition process. Given the volume of data that these analyses involve, it was necessary to develop robust data acquisition, storing and management strategies. The use of Geographical Information System environments together with customised Python-based programs and cloud-based distribution permitted to assemble urban databases for facilitating field data acquisition, performing vulnerability and damage calculations, and representing outcomes. This development was the base for performing a second large-scale assessment in selected municipalities of the state of Morelos (Mexico). The characterisation of the seismic vulnerability of more than 160 buildings permitted to assess the representativeness of the parametric vulnerability approach by comparing the theoretical damage estimations against the damages observed after the Puebla-Morelos 2017 Earthquakes. Such comparison is the base for performing a Machine Learning assisted process of calibration and adjustment, representing a feasible strategy for calibrating these vulnerability models by using Machine-Learning algorithms and the empirical evidence of damage in post-seismic scenarios.This work was partly financed by FCT/MCTES through national funds (PIDDAC) under the R&D Unit Institute for Sustainability and Innovation in Structural Engineering (ISISE), reference UIDB/04029/2020. This research had financial support provided by the Portuguese Foundation of Science and Technology (FCT) through the Analysis and Mitigation of Risks in Infrastructures (InfraRisk) program under the PhD grant PD/BD/150385/2019

Unveiling the frontiers of deep learning: innovations shaping diverse domains

Deep learning (DL) enables the development of computer models that are capable of learning, visualizing, optimizing, refining, and predicting data. In recent years, DL has been applied in a range of fields, including audio-visual data processing, agriculture, transportation prediction, natural language, biomedicine, disaster management, bioinformatics, drug design, genomics, face recognition, and ecology. To explore the current state of deep learning, it is necessary to investigate the latest developments and applications of deep learning in these disciplines. However, the literature is lacking in exploring the applications of deep learning in all potential sectors. This paper thus extensively investigates the potential applications of deep learning across all major fields of study as well as the associated benefits and challenges. As evidenced in the literature, DL exhibits accuracy in prediction and analysis, makes it a powerful computational tool, and has the ability to articulate itself and optimize, making it effective in processing data with no prior training. Given its independence from training data, deep learning necessitates massive amounts of data for effective analysis and processing, much like data volume. To handle the challenge of compiling huge amounts of medical, scientific, healthcare, and environmental data for use in deep learning, gated architectures like LSTMs and GRUs can be utilized. For multimodal learning, shared neurons in the neural network for all activities and specialized neurons for particular tasks are necessary.Comment: 64 pages, 3 figures, 3 table

Surveying and Three-Dimensional Modeling for Preservation and Structural Analysis of Cultural Heritage

Dense point clouds can be used for three important steps in structural analysis, in the field of cultural heritage, regardless of which instrument it was used for acquisition data. Firstly, they allow deriving the geometric part of a finite element (FE) model automatically or semi-automatically. User input is mainly required to complement invisible parts and boundaries of the structure, and to assign meaningful approximate physical parameters. Secondly, FE model obtained from point clouds can be used to estimate better and more precise parameters of the structural analysis, i.e., to train the FE model. Finally, the definition of a correct Level of Detail about the three-dimensional model, deriving from the initial point cloud, can be used to define the limit beyond which the structural analysis is compromised, or anyway less precise. In this work of research, this will be demonstrated using three different case studies of buildings, consisting mainly of masonry, measured through terrestrial laser scanning and photogrammetric acquisitions. This approach is not a typical study for geomatics analysis, but its challenges allow studying benefits and limitations. The results and the proposed approaches could represent a step towards a multidisciplinary approach where Geomatics can play a critical role in the monitoring and civil engineering field. Furthermore, through a geometrical reconstruction, different analyses and comparisons are possible, in order to evaluate how the numerical model is accurate. In fact, the discrepancies between the different results allow to evaluate how, from a geometric and simplified modeling, important details can be lost. This causes, for example, modifications in terms of mass and volume of the structure