scholarly journals Fully Automated Pose Estimation of Historical Images in the Context of 4D Geographic Information Systems Utilizing Machine Learning Methods

2021 ◽  
Vol 10 (11) ◽  
pp. 748
Author(s):  
Ferdinand Maiwald ◽  
Christoph Lehmann ◽  
Taras Lazariv
Keyword(s):  
Information Systems ◽  
Geographic Information Systems ◽  
Image Retrieval ◽  
Pose Estimation ◽  
Large Scale ◽  
Feature Matching ◽  
Geographic Information ◽  
Content Based Image Retrieval ◽  
Matching Methods ◽  
Historical Images

The idea of virtual time machines in digital environments like hand-held virtual reality or four-dimensional (4D) geographic information systems requires an accurate positioning and orientation of urban historical images. The browsing of large repositories to retrieve historical images and their subsequent precise pose estimation is still a manual and time-consuming process in the field of Cultural Heritage. This contribution presents an end-to-end pipeline from finding relevant images with utilization of content-based image retrieval to photogrammetric pose estimation of large historical terrestrial image datasets. Image retrieval as well as pose estimation are challenging tasks and are subjects of current research. Thereby, research has a strong focus on contemporary images but the methods are not considered for a use on historical image material. The first part of the pipeline comprises the precise selection of many relevant historical images based on a few example images (so called query images) by using content-based image retrieval. Therefore, two different retrieval approaches based on convolutional neural networks (CNN) are tested, evaluated, and compared with conventional metadata search in repositories. Results show that image retrieval approaches outperform the metadata search and are a valuable strategy for finding images of interest. The second part of the pipeline uses techniques of photogrammetry to derive the camera position and orientation of the historical images identified by the image retrieval. Multiple feature matching methods are used on four different datasets, the scene is reconstructed in the Structure-from-Motion software COLMAP, and all experiments are evaluated on a newly generated historical benchmark dataset. A large number of oriented images, as well as low error measures for most of the datasets, show that the workflow can be successfully applied. Finally, the combination of a CNN-based image retrieval and the feature matching methods SuperGlue and DISK show very promising results to realize a fully automated workflow. Such an automated workflow of selection and pose estimation of historical terrestrial images enables the creation of large-scale 4D models.

Geographic Information Systems for Tracking Bridge Component Inspection and Repairs

1997 ◽  
Vol 1575 (1) ◽  
pp. 60-65
Author(s):  
Raymond D. Thierrin
Keyword(s):  
Information Systems ◽  
Geographic Information Systems ◽  
Computer Aided Design ◽  
Large Scale ◽  
Geographic Information ◽  
Digital Information ◽  
Bridge Rehabilitation ◽  
Digital Format ◽  
Management Procedures ◽  
Available Technology

Bridge component inspection and repair information has been traditionally collected on paper forms by field personnel and stored in project files. Because of the industrywide use of computer-aided design and drafting technology in bridge rehabilitation design, digital information for bridge components is often available as a by-product of the design process. In addition, projects are becoming more sophisticated and, as a result, the construction field office is becoming more automated. It is now possible to automate field data collection and management procedures so that information can be captured in a digital format in the field and used throughout the construction documentation process. The available technology includes pen-based computers, pen-enabled database software, and digital color cameras, all of which can be integrated into systems that are easily used by field inspection personnel. By using databases and geographic information systems, inspectors and engineers can readily review component information and track the progress of repairs for large-scale rehabilitation projects.

The modifiable areal unit problem (MAUP) in physical geography

2007 ◽  
Vol 31 (5) ◽  
pp. 471-479 ◽  
Author(s):  
Shawna J. Dark ◽  
Danielle Bram
Keyword(s):  
Remote Sensing ◽  
Information Systems ◽  
Statistical Analysis ◽  
Geographic Information Systems ◽  
Large Scale ◽  
Geographic Information ◽  
Physical Geography ◽  
Modifiable Areal Unit Problem ◽  
Areal Unit ◽  
Quantitative Studies

Of particular importance to the study of large-scale phenomena in physical geography is the modifiable areal unit problem ( MAUP). While often viewed as only a problem in human geography (particularly demographic studies), the MAUP is an issue for all quantitative studies in geography of spatial phenomena (Openshaw and Taylor, 1979). Increasingly, remote sensing and Geographic Information Systems ( GIS) are being used to assess the distribution of phenomena from a large scale. These phenomena are modelled using areal units that can take any shape or size resulting in complications with statistical analysis related to both the scale and method used to create the areal units. In this paper, we define the modifiable areal unit problem, present examples of when it is a problem in physical geography studies, and review some potential solutions to the problem. Our aim is to increase awareness of this complicated issue and to promote further discussion and interest in this topic.

Visualization of Large-Scale Narrative Data Describing Human Error

2017 ◽  
Vol 59 (4) ◽  
pp. 520-534 ◽  
Author(s):  
William J. Irwin ◽  
Saul D. Robinson ◽  
Stephen M. Belt
Keyword(s):  
Information Systems ◽  
Geographic Information Systems ◽  
Latent Semantic Analysis ◽  
Human Error ◽  
Large Scale ◽  
Semantic Analysis ◽  
Difficult Problem ◽  
Geographic Information ◽  
Data Sets ◽  
Systems Software

Objective Introduced is a visual data exploration technique for compiling, reducing, organizing, visually rendering, and filtering text-based narratives for detailed analysis. Background The analysis of data sets provides an increasingly difficult problem. The method of visual representation is considered an effective tool in many applications. The focus of this study was to determine if a latent semantic analysis–based projection of narrative data into a geographic information systems software program provided a useful tool for reducing and organizing large sums of narrative data for analysis. Method This approach utilizes latent semantic analysis to reduce narratives to a high-dimensional vector, truncates the vector to a two-dimensional projection through application of isometric mapping, and then visually renders the result with geographic information systems software. This method is demonstrated on aviation self-reported safety narratives sourced from the Aviation Safety Reporting System. Results Thematic regions from the corpus are illustrated along with the first five topics identified. Conclusion Shown is the ability to assimilate a large number of narratives, identify contextual themes, recognize common events and outliers, and organize resultant topics. Application Large narrative-based data sets present in aviation and other domains may be visualized to facilitate efficient analysis, enhance comprehension, and improve safety.

scholarly journals The Water–Energy–Food Nexus Discovery Map: Linking Geographic Information Systems, Academic Collaboration, and Large-Scale Data Visualization

2021 ◽  
Vol 13 (9) ◽  
pp. 5220
Author(s):  
Matthew Arenas ◽  
Pandara Valappil Femeena ◽  
Rachel A. Brennan
Keyword(s):  
Information Systems ◽  
Geographic Information Systems ◽  
Policy Development ◽  
Large Scale ◽  
Geographic Information ◽  
Specific Information ◽  
Technological Advancement ◽  
Data Resource ◽  
Advanced Search ◽  
Initiative Program

The Water–Energy–Food (WEF) Nexus framework for holistic sustainable development has spawned independent and academic communities around the globe that utilize the framework in research, implementation, policy development, and technological advancement. These communities, however, are geographically and topically segmented and lack large-scale databasing that clearly catalogs and classifies their work. Recognizing this need, the WEF Nexus Strategic Initiative program at The Pennsylvania State University has developed the WEF Nexus Discovery Map utilizing the Arc Geographic Information Systems’ (GIS) Online Dashboard creation toolkit. In real time, users are able to select from 5040 different combinations of filters with the ease of a few button pushes and see projects pop up or disappear from the map located on the dashboard. Projects can then be clicked on to view their specific information, such as the institution that produced the work, local collaborators, relevant web page, and point of contact. The WEF Nexus Discovery Map demonstrates the early new-age of data resource management with the intersection of visuals, advanced search with built-in filters, and community-driven data collection to provide users with exact needs and connections to better facilitate and deploy the holistic sustainability framework of the WEF Nexus.

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