Metadata Information Research Articles

Application of AI-Helped Image Classification of Fish Images: An iDigBio dataset example

Artificial Intelligence (AI) becomes more prevalent in data science as well as in areas of computational science. Commonly used classification methods in AI can also be used for unorganized databases, if a proper model is trained. Most of the classification work is done on image data for purposes such as object detection and face recognition. If an object is well detected from an image, the classification may be done to organize image data. In this work, we try to identify images from an Integrated Digitized Biocollections (iDigBio) dataset and to classify these images to generate metadata to use as an AI-ready dataset in the future. The main problem of the museum image datasets is the lack of metadata information on images, wrong categorization, or poor image quality. By using AI, it maybe possible to overcome these problems. Automatic tools can help find, eliminate or fix these problems. For our example, we trained a model for 10 classes (e.g., complete fish, photograph, notes/labels, X-ray, CT (computerized tomotography) scan, partial fish, fossil, skeleton) by using a manually tagged iDigBio image dataset. After training a model for each for class, we reclassified the dataset by using these trained models. Some of the results are given in Table 1. As can be seen in the table, even manually classified images can be identified as different classes, and some classes are very similar to each other visually such as CT scans and X-rays or fossils and skeletons. Those kind of similarities are very confusing for the human eye as well as AI results.

Characterizing Errors Using Satellite Metadata for Eco‐Hydrological Model Calibration

AbstractUnderstanding the origins of errors between model predictions and catchment observations is a critical element in hydrologic model calibration and uncertainty estimation. Difficulties arise because there are a variety of error sources but only one measure of the total residual error between model predictions and catchment observations. One promising approach is to collect extra information a priori to characterize the data error before calibration. We implement here a new model calibration strategy for an ecohydrological model, using the satellite metadata information as a means to inform the model priors, to decompose data error from total residual error. This approach, referred to as Bayesian ecohydrological error model (BEEM), is first examined in a synthetic setting to establish its validity, and then applied to three real catchments across Australia. Results show that (a) BEEM is valid in a synthetic setting, as it can perfectly ascertain the true underlying error; (b) in real catchments the model error is reduced when utilizing the observation error variance as added error contributing to total error variance, while the magnitude of total residual error is more robust when utilizing metadata about the data quality proportionality as the basis for assigning total error variance; (c) BEEM improves model calibration by estimating the model error appropriately and estimating the uncertainty interval more precisely. Overall, our work demonstrates a new approach to collect prior error information in satellite metadata and reveals the potential for fully utilizing metadata about error sources in uncertainty estimation.

Collection insight and interconnectivity through artificial intelligence image analysis: A collaboration with the National Archives of Estonia

Descriptive metadata information is paramount to creating a holistic historical record for researchers, scholars and the general public, as well as other institutions to use both internally and externally. Metadata contextualise image-based collections, providing insight into the individuals, objects and stories contained within them, allowing for the sharing and cross-referencing of information and collections. However, gathering descriptive metadata for image collections is time-consuming and costly to generate, often relying the knowledge of a single person. And yet, if researchers do not extract, catalogue and link this information to the images, valuable context can be lost, obscured by the sheer volume of images that one would have to review manually in order to find specific content. This paper demonstrates how the application of artificial intelligence (AI) image analysis offers an alternative to the traditional labour-intensive manual approach and provides important identifications within collections that allow for further research, collaboration and accessibility. The paper describes how, in partnership with the National Archives of Estonia, we digitised over 100,000 film frames and glass-plate negatives, collected from 22 different institutions around the country, and applied AI image analysis to the digital assets in order to take the first step in successfully identifying individuals within the collections, unlocking their stories, interconnecting these institutions’ collections and the broader cultural heritage community.

Near real-time predictions of renewable electricity production at substation level via domain adaptation zero-shot learning in sequence

With the urgency of the transition to a resilient low-carbon economy, the monitoring and prediction of regional renewable energy generation over time have become increasingly important. The difficulties of renewable energy data transfer between multiple stakeholders have also caused elevated the need for more trustworthy data analytics within the energy grid. However, only a few voltage transformation facilities in the grid (i.e. substations) contain complete information about the renewable energy generated within the region. A large number of incomplete-information substations with fully-missing renewable energy data limits the analysis and policy-making related to renewable energy. This work studies the potential to transfer information from perfect-information substations to incomplete-information substations with fully-missing renewable energy data. To preserve the practicality of renewable energy prediction, a domain adaptation for zero-shot learning in sequence (DAZLS) strategy is proposed for fully-missing renewable energy prediction in substations. DAZLS is a model agnostic technique which can utilize any base model within its framework for the prediction of renewable energy generation. Using total measured power and weather information (solar irradiation and wind speed) in information-complete substations (8831 timestamps for 10 substations) within the Netherlands, we developed a model to predict solar and wind power from energy producers associated with information incomplete substations via additional real-time weather data, metadata information (e.g. geospatial position, existence and capacity of renewable facilities). Using DAZLS, the average root-mean-squared error for prediction (RMSEP) is 0.07, while that of a default transfer learning model is 0.70. This meant that renewable energy sources in information-incomplete substations could be reliably monitored using weather data, meta-data and physical data, resulting in lesser investment in power meters. This approach was demonstrated for the highest frequency prediction possible in the grid, with a near-real-time frequency of 15 min. Our method can be effectively used for renewable energy grid optimization, planning and analysis.

Novel machine learning based authentication technique in VANET system for secure data transmission

<p>Adaptive transport technologies based on vehicular ad hoc networks (VANET) has proven considerable potential in light of the developing expansion of driver assistance and automobile telecommunication systems. However, confidentiality and safety are the vital challenges in vehicular ad hoc networks which could be seriously impaired by malicious attackers. While protecting vehicle privacy from threats, it is imperative to stop internal vehicles from putting out bogus messages. Considering these issues, a novel machine learning based message authentication combined with blockchain and inter planetary file system (IPFS) is proposed to achieve message dissemination in a secured way. Blockchain is the emerging technology which attempts to solve these problems by producing tamper proof events of records in a distributed environment and inter planetary file system used in the framework is a protocol designed to store the event with content addressability. Along with this combined technology, the source metadata information collected from the inter planetary file system is stored via a smart contract and uploaded to the distributed ledger technology (DLT). For performing event authentication, K-means clustering and support vector machine (SVM) classifier is employed in this framework. K-means clustering performs clustering of vehicles and it is marked malicious or not malicious. After clustering, support vector machine classifier detects the malicious event messages. By this way, the malicious messages are identified and it is dropped. Only the secure messages are forwarded in the network. Finally, our approach is capable of creating a safe and decentralized vehicular ad hoc network architecture with accountability and confidentiality through theoretical study and simulations.</p>

Tissue proteomics repositories for data reanalysis.

We are approaching the third decade since the establishment of the very first proteomics repositories back in the mid-'00s. New experimental approaches and technologies continuously enrich the field while producing vast amounts of mass spectrometry data. Together with initiatives to establish standard terminology and file formats, proteomics is rapidly transforming into a mature component of systems biology. Here we describe the ProteomeXchange consortium repositories. We specifically search, collect and evaluate public human tissue datasets (categorized as "complete" by the repository) submitted in 2015-2022, to both map the existing information and assess the data set reusability. Human tissue data are variably represented in the repositories reviewed, ranging between 10% and 25% of the total data submitted, with cancers being the most represented, followed by neuronal and cardiovascular diseases. About half of the retrieved data sets were found to lack annotations or metadata necessary to directly replicate the analysis. This poses a rough challenge to data reusability and highlights the need to increase awareness of the mage-tab file format for metadata in the community. Overall, proteomics repositories have evolved greatly over the past 7 years, as they have grown in size and become equipped with various powerful applications and tools that enable data searching and analytical tasks. However, to make the most of this potential, priority must be given to finding ways to secure detailed metadata for each submission, which is likely the next major milestone for proteomics repositories.

Comparison of Corecorded Analog and Digital Systems for Characterization of Responses and Uncertainties

Abstract One of the most prominent challenges related to legacy seismic data is determining how these data can be appropriately used in modern research applications. The wide variety of instrumentation used in the analog era, the format of recording on paper wrapped around a helicorder drum, and limited metadata information introduces ambiguities that are not typical of modern digital data. Therefore, techniques must be developed to help characterize uncertainties in legacy data. This article presents an analysis that compares corecorded signals from two instruments—a Trillium Compact or Press-Ewing (PE) seismometer for sensing ground motion and two recording systems: a modern Q330 digitizer or heated-stylus system. Analyses of the recordings in both time and frequency domains indicate time uncertainty on the order of one second, identify a flat response in a 10–60 s band for the PE and drum recorder, and highlight how specific features of scans and paper seismograms (e.g., repeated portions of scans and line thickness) can cause timing jumps or reduced trace amplitude.

Book Review: Metadata for Digital Collections, Second Edition

The ability to create digital collections has become more accessible to libraries and cultural heritage institutions of all sizes over the last few years, making it a good time to release the second edition of Steven Jack Miller’s Metadata for Digital Collections. Miller is successful in his goal to write a textbook that is “accessible to beginners and library and information studies students as well as experienced professionals with little formal metadata training” (xxl). Throughout the book he introduces fundamental concepts that everyone developing a digital collection will need in order to be successful. Complex concepts—such as interoperability, linked data, and controlled vocabulary—are introduced early in the book in a manner that is appropriate for someone who is unfamiliar with the topics. Later chapters examine each of these topics in-depth providing a solid grounding and understanding of these concepts. In addition to writing plainly, Miller includes many examples that clearly illustrate the concepts being introduced. He also created a number of useful sidebars; I particularly found the “Typology of Metadata Standards” (15) and “Five Ways to Improve Metadata Quality and Interoperability: Summary Overview” (316) sidebars helpful in how they summarize the information in their respective sections which will make it easy for me to reference in the future.

Epoxy: ACID Transactions across Diverse Data Stores

Developers are increasingly building applications that incorporate multiple data stores, for example to manage heterogeneous data. Often, these require transactional safety for operations across stores, but few systems support such guarantees. To solve this problem, we introduce Epoxy, a protocol for providing transactions across heterogeneous data stores. We make two contributions. First, we adapt multi-version concurrency control to a cross-data store setting, storing versioning information in record metadata and filtering reads with predicates on metadata so they only see record versions in a global transaction snapshot. Second, we show our design enables an atomic commit protocol that does not require data stores implement the participant protocol of two-phase commit, requiring only durable writes. We implement Epoxy for five data stores: Postgres, Elasticsearch, MongoDB, Google Cloud Storage, and MySQL. We evaluate it by adapting TPC-C and microservice workloads to a multi-data store environment. We find it has comparable performance to the distributed transaction protocol XA on TPC-C while providing stronger guarantees like isolation, and has overhead of <10% compared to a non-transactional baseline on read-mostly microservice workloads and 72% on write-heavy workloads.

How Do Users Examine Online Messages to Determine If They Are Credible? An Eye-Tracking Study of Digital Literacy, Visual Attention to Metadata, and Success in Misinformation Identification

Although previous studies examined the role of digital literacy in successful identification of misinformation, scant scholarly attention has been given to users’ attention to metadata as informative areas that attest to message credibility. This study introduces a novel approach and methodology to contribute to our understanding of how users evaluate and identify misinformation, and the relationship between users’ ocular attention to metadata, misinformation identification, and digital literacy. In an eye-tracking study, participants were asked to rate the credibility of online messages posted on social media and web pages. Throughout the session, participants’ eye movements were recorded. The results indicate that digital literacy predicts successful identification of online misinformation, as well as webpage scan patterns, specifically devoting attention and focusing gaze at metadata areas that provide cues attesting to the credibility of the messages. In addition, successful identification of misinformation is positively linked to ocular attention to information metadata. In other words, technology-savvy users devote more attention to information metadata, which leads to better identification of misinformation, and they are also directly more successful at identifying misinformation online.

Enhancing accessibility for the blind and visually impaired: Presenting semantic information in PDF tables

The ubiquitous and layout-friendly PDF documents have multiple elements for disseminating knowledge and require visual cues for interpretation. Users with visual impairments depend on assistive technologies that do not offer comprehensive information about non-textual elements such as tables. This emphasizes the importance of making PDF documents and their tables accessible to everyone. Hence, this study proposes to unveil the hidden semantics of PDF tables to blind and visually inspired people for a comprehensive understanding. Therefore, a heuristic approach is used to extract the explicit and implicit features including metadata, functional, structural, content, and contextual information. The extracted features are utilized to provide insights into the PDF table to the intended users by providing a lay summary before navigating the rows and columns. The proposed solution is evaluated quantitatively for the visual features of a table including captions, headers, and structures, and obtained encouraging results using precision, recall, and F-score. Additionally, qualitative evaluation is conducted by domain experts to assess the functionalities of the developed prototype against the developed heuristics and the adherence to standard accessibility guidelines of Web Content Accessibility Guidelines (WCAG 2.1) and ICT accessibility section 508. The extracted features can be utilized in various downstream applications, such as table classification, integration, searching, recommendation, and gerontechnology. By providing insights into PDF tables, this research serves as a starting point to improve accessibility for blind or visually impaired people.

A Qualitative Analysis on Pesantren Economic

Based on a report from the Ministry of Religion, West Java is one of the provinces that contributes the most number of Pesantren, namely 8,343 Pesantren or about 30.92 per cent of the total national Pesantren. This shows that pesantren, as an institution with entrepreneurial characteristics, can contribute to the concept of a sustainable business by providing benefits from the social, economic and environmental aspects. This study aims to determine the economic development of Pesantren in the last 21 years, from 2001 to 2022. The method used is a qualitative analysis of secondary data in the form of metadata with the keywords pesantren economics from 403 Dimensions.ai indexed publications, then processed using Nvivo 12 Plus software. The results showed that the number of publications on the economic development of Pesantren continued to increase. From the results of the exploratory review and visualization of the pattern of pesantren economic themes, it was found that there were four research lines with discussions on pesantren and community empowerment, pesantren and cooperatives, pesantren and entrepreneurship, as well as pesantren and Micro Waqf Bank (BWM). In addition, this study shows that most of the research on the economics of Pesantren is related to the words economic, business, management, development, and program. As for the business management system and its development, it is still a challenge for pesantren. Thus, this research can be used as an essential reference to see how the graphic visualization of the development of research trends with the theme of Islamic boarding school economics in published scientific research so that experts can further develop it. This research is expected to help expand academic studies related to the economics of Pesantren.

MSdb: An integrated expression atlas of human musculoskeletal system

The global prevalence and burden of musculoskeletal (MSK) disorders are immense. Advancements in next-generation sequencing (NGS) have generated vast amounts of data, accelerating the research of pathological mechanisms and the development of therapeutic approaches for MSK disorders. However, scattered datasets across various repositories complicate uniform analysis and comparison. Here, we introduce MSdb, a database for visualization and integrated analysis of next-generation sequencing data from human musculoskeletal system, along with manually curated patient phenotype data. MSdb provides various types of analysis, including sample-level browsing of metadata information, gene/miRNA expression, and single-cell RNA-seq dataset. In addition, MSdb also allows integrated analysis for cross-samples and cross-omics analysis, including customized differentially expressed gene/microRNA analysis, microRNA-gene network, scRNA-seq cross-sample/disease integration, and gene regulatory network analysis. Overall, systematic categorizing, standardized processing, and freely accessible knowledge features MSdb a valuable resource for MSK research community.

Co-Clinical Imaging Metadata Information (CIMI) for Cancer Research to Promote Open Science, Standardization, and Reproducibility in Preclinical Imaging.

Preclinical imaging is a critical component in translational research with significant complexities in workflow and site differences in deployment. Importantly, the National Cancer Institute's (NCI) precision medicine initiative emphasizes the use of translational co-clinical oncology models to address the biological and molecular bases of cancer prevention and treatment. The use of oncology models, such as patient-derived tumor xenografts (PDX) and genetically engineered mouse models (GEMMs), has ushered in an era of co-clinical trials by which preclinical studies can inform clinical trials and protocols, thus bridging the translational divide in cancer research. Similarly, preclinical imaging fills a translational gap as an enabling technology for translational imaging research. Unlike clinical imaging, where equipment manufacturers strive to meet standards in practice at clinical sites, standards are neither fully developed nor implemented in preclinical imaging. This fundamentally limits the collection and reporting of metadata to qualify preclinical imaging studies, thereby hindering open science and impacting the reproducibility of co-clinical imaging research. To begin to address these issues, the NCI co-clinical imaging research program (CIRP) conducted a survey to identify metadata requirements for reproducible quantitative co-clinical imaging. The enclosed consensus-based report summarizes co-clinical imaging metadata information (CIMI) to support quantitative co-clinical imaging research with broad implications for capturing co-clinical data, enabling interoperability and data sharing, as well as potentially leading to updates to the preclinical Digital Imaging and Communications in Medicine (DICOM) standard.

SCINOBO: a novel system classifying scholarly communication in a dynamically constructed hierarchical Field-of-Science taxonomy.

Classifying scientific publications according to Field-of-Science taxonomies is of crucial importance, powering a wealth of relevant applications including Search Engines, Tools for Scientific Literature, Recommendation Systems, and Science Monitoring. Furthermore, it allows funders, publishers, scholars, companies, and other stakeholders to organize scientific literature more effectively, calculate impact indicators along Science Impact pathways and identify emerging topics that can also facilitate Science, Technology, and Innovation policy-making. As a result, existing classification schemes for scientific publications underpin a large area of research evaluation with several classification schemes currently in use. However, many existing schemes are domain-specific, comprised of few levels of granularity, and require continuous manual work, making it hard to follow the rapidly evolving landscape of science as new research topics emerge. Based on our previous work of scinobo, which incorporates metadata and graph-based publication bibliometric information to assign Field-of-Science fields to scientific publications, we propose a novel hybrid approach by further employing Neural Topic Modeling and Community Detection techniques to dynamically construct a Field-of-Science taxonomy used as the backbone in automatic publication-level Field-of-Science classifiers. Our proposed Field-of-Science taxonomy is based on the OECD fields of research and development (FORD) classification, developed in the framework of the Frascati Manual containing knowledge domains in broad (first level(L1), one-digit) and narrower (second level(L2), two-digit) levels. We create a 3-level hierarchical taxonomy by manually linking Field-of-Science fields of the sciencemetrix Journal classification to the OECD/FORD level-2 fields. To facilitate a more fine-grained analysis, we extend the aforementioned Field-of-Science taxonomy to level-4 and level-5 fields by employing a pipeline of AI techniques. We evaluate the coherence and the coverage of the Field-of-Science fields for the two additional levels based on synthesis scientific publications in two case studies, in the knowledge domains of Energy and Artificial Intelligence. Our results showcase that the proposed automatically generated Field-of-Science taxonomy captures the dynamics of the two research areas encompassing the underlying structure and the emerging scientific developments.

Forensik Video Pada CCTV Menggunakan Framework Generic Computer Forensics Investigation Model (GCFIM)

The level of crime recorded by CCTV (Closed Circuit Televison) cameras continues to increase, criminals who are proven to have committed certain criminal acts will be punished in accordance with statutory regulations. The problem is how a case recorded on a CCTV camera can be used as digital evidence at trial. And how to handle it if there is evidence that has been manipulated or changed by irresponsible parties to eliminate traces of digital evidence. Digital evidence is closely related to ensuring the security, privacy and integrity of data when following the stages of the forensic digital identification process. The identification model using the Generic Computer Forensic Investigation Model (GCFIM) Framework is used for the investigation stage in finding information related to video recording metadata on CCTV cameras, in order to provide structured and valid information to be accepted in court as digital evidence. The results of this study obtained information on the data testing process using the Generic Computer Forensic Investigation Model (GCFIM) framework and for the metadata analysis process using two tools, namely exiftool and mediainfo. Testing is done on the original video and tempering video. Regarding the metadata of the original video and tempering video, there is a very significant difference in the size of the original video 4.6Mb while the tempering video is 4.2Mb, the duration of the original video is 38s for the tempering video 32s and the modification date / time for the original video 2020:03:21 while the tempering video is 2022:08:04. It can be concluded that exiftool and mediainfo can be utilized to obtain metadata information from data, with the aim of strengthening digital evidence at the time of trial regarding the validity of the data

Approaches and tools for user-driven provenance and data quality information in spatial data infrastructures

ABSTRACT Geospatial data are fundamental in most global-change and sustainability-related domains. However, readily accessible information on data quality and provenance is often missing or hardly accessible for users due to technical or perceptual barriers, for example, due to unstructured metadata information or missing references. Within an interdisciplinary process encompassing perspectives of data users, data producers, and software developers, we identified major needs to facilitate effective fitness-for-use assessments by data users and developed approaches to address these. We provided a stylized analysis of large-scale land use data to showcase selected approaches. To support data users, interoperable quality and provenance information need to be meaningfully represented. Data producers need efficient workflows and tools supporting them in creating high-quality, structured and detailed quality and provenance information. Our newly developed approaches to increase the availability of structured metadata synthesize new and existing tools to extract metadata or to generate provenance data during processing. Within our approaches to improve interoperability and accessibility we present novel tools to support (i) the creation of curated and linked registers of data quality indicators and thematic terms, and (ii) linked visualization of data quality and provenance information. Following our approaches increases transparency, facilitates fitness-for-use assessments, and ultimately improves research quality.

Assessing key influences on the distribution and life-history of Arctic and boreal Calanus: are online databases up to the challenge?

Despite the importance of calanoid copepods to healthy ecosystem functioning of the Arctic Ocean and Subarctic Seas, many aspects of their biogeography, particularly in winter months, remain unresolved. At the same time, online databases that digitize species distribution records are growing in popularity as a tool to investigate ecological patterns at macro scales. The value of such databases for Calanus research requires investigation - the long history of Calanus sampling holds promise for such databases, while conditions at high latitudes may impose limits through spatial and temporal biases. We collated records of three Calanus species (C. finmarchicus, C. glacialis, and C. hyperboreus) from the Ocean Biodiversity Information System (OBIS) and the Global Biodiversity Information Facility (GBIF) providing over 230,000 unique records spanning 150 years and over 100 individual datasets. After quality control and cleaning, the latitudinal and vertical distribution of occurrences were explored, as well as the completeness of informative metadata fields. Calanus sampling was found to be temporally and spatially biased towards surfacemost layers (<10m) in spring and summer. Only 3.5% of records had an average collection depth ≥400m, approximately half of these in months important for diapause. Just over 40% of records lacked associated information on sampling protocol while 11% of records lacked life-stage information. OBIS data contained fields for maximum and minimum collection depth and so were subset into discrete “shallow summer” and “deep winter” life cycle phases and matched to sea-ice and temperature conditions. 23% of OBIS records north of 66° latitude were located in regions of seasonal sea-ice presence and occurrences show species-specific thermal optima during the shallow summer period. The collection depth of C. finmarchicus was significantly different to C. hyperboreus during the deep winter. Overall, online databases contain a vast number of Calanus records but sampling biases should be acknowledged when they are used to investigate patterns of biogeography. We advocate efforts to integrate additional data sources within online portals. Particular gaps to be filled by existing or future collections are (i) widening the spatial extent of sampling during spring/summer months, (ii) increasing the frequency of sampling during winter, particularly at depths below 400m, and (iii) improving the quality, quantity and consistency of metadata reporting.

Recent flex power changes

The redistribution of the transmit power among different GPS signal components is called flex power, and it is implemented on modernized GPS satellites starting with Block IIR-M. The purpose of the flex power is to increase signal protection against jamming. It has been mostly active since the beginning of 2017 in different flex power modes and is visible in the carrier-to-noise density ratio (C/N0) observations as stepwise changes. Especially after the beginning of 2020, the flex power modes changed frequently. We summarize these changes and study their impact on differential code biases (DCBs) which is 0.3 ns on average for L1 and L2 intra-frequency DCBs. The flex power is analyzed daily using C/N0 measurements of the globally distributed GNSS observations network from January 1, 2020 until February 28, 2022. The flex power activation times are written into a dedicated block of the SINEX metadata format.

Giving Historical Photographs a New Perspective: Introducing Camera Orientation Parameters as New Metadata in a Large-Scale 4D Application

The ongoing digitization of historical photographs in archives allows investigating the quality, quantity, and distribution of these images. However, the exact interior and exterior camera orientations of these photographs are usually lost during the digitization process. The proposed method uses content-based image retrieval (CBIR) to filter exterior images of single buildings in combination with metadata information. The retrieved photographs are automatically processed in an adapted structure-from-motion (SfM) pipeline to determine the camera parameters. In an interactive georeferencing process, the calculated camera positions are transferred into a global coordinate system. As all image and camera data are efficiently stored in the proposed 4D database, they can be conveniently accessed afterward to georeference newly digitized images by using photogrammetric triangulation and spatial resection. The results show that the CBIR and the subsequent SfM are robust methods for various kinds of buildings and different quantity of data. The absolute accuracy of the camera positions after georeferencing lies in the range of a few meters likely introduced by the inaccurate LOD2 models used for transformation. The proposed photogrammetric method, the database structure, and the 4D visualization interface enable adding historical urban photographs and 3D models from other locations.