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Record W2045643953 · doi:10.1007/s12205-012-1272-7

An automated system for the creation of an urban infrastructure 3D model using image processing techniques

2011· article· en· W2045643953 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueKSCE Journal of Civil Engineering · 2011
Typearticle
Languageen
FieldEnvironmental Science
TopicRemote Sensing and LiDAR Applications
Canadian institutionsUniversity of AlbertaVanguard College
FundersMinistry of Education, Science and TechnologyNational Research Foundation
KeywordsComputer scienceArchitectureImage (mathematics)DatabaseImage processingGeographic information systemArtificial intelligenceGeographyRemote sensing

Abstract

fetched live from OpenAlex

Image database creation for infrastructure management is an interdisciplinary endeavor in computer vision, database, and structural engineering. In response to increasing demands for multimedia information in infrastructure management, image databases are becoming an ever more active research area. This paper proposes an automated system for creating an urban infrastructure 3D model using an image database; the system is built by images shot in public areas to record changes of urban infrastructure in three-dimensional (3D) space, such as the addition of new buildings, new overpasses, loss of traffic signs, and growth/change of trees. The system architecture is presented with an emphasis on a 3D information capture and extraction module. Initial experiments with the 3D information capture module show that the proposed system has the potential to efficiently develop a large-scale 3D model of the streets of a municipality.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.455
Threshold uncertainty score0.255

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.009
GPT teacher head0.239
Teacher spread0.230 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it